BAY Bayesian 電子書籍

BAY
Bayesian
電子書籍

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本・雑誌・コミック (288) (BAY Bayesian 電子書籍)
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商品	説明	価格
Bayesian Analysis with Python A practical guide to probabilistic modeling【電子書籍】[ Osvaldo Martin ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Conduct Bayesian data analysis with step-by-step guidance＜/li＞＜li＞Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling＜/li＞＜li＞Enhance your learning with best practices through sample problems and practice exercises＜/li＞＜li＞Purchase of the print or Kindle book includes a free PDF eBook.＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection. In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets. By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Build probabilistic models using PyMC and Bambi＜/li＞＜li＞Analyze and interpret probabilistic models with ArviZ＜/li＞＜li＞Acquire the skills to sanity-check models and modify them if necessary＜/li＞＜li＞Build better models with prior and posterior predictive checks＜/li＞＜li＞Learn the advantages and caveats of hierarchical models＜/li＞＜li＞Compare models and choose between alternative ones＜/li＞＜li＞Interpret results and apply your knowledge to real-world problems＜/li＞＜li＞Explore common models from a unified probabilistic perspective＜/li＞＜li＞Apply the Bayesian framework's flexibility for probabilistic thinking＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞If you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	4,304円
Bayesian Models of Perception and Action An Introduction【電子書籍】[ Wei Ji Ma ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action.＜/strong＞＜/p＞＜p＞Many forms of perception and action can be mathematically modeled as probabilisticーor Bayesianーinference, a method used to draw conclusions from uncertain evidence. According to these models, the human mind behaves like a capable data scientist or crime scene investigator when dealing with noisy and ambiguous data. This textbook provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action. Featuring extensive examples and illustrations, ＜em＞Bayesian Models of Perception and Action＜/em＞ is the first textbook to teach this widely used computational framework to beginners.＜/p＞＜ul＞＜li＞Introduces Bayesian models of perception and action, which are central to cognitive science and neuroscience＜/li＞＜li＞Beginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex concepts＜/li＞＜li＞Broad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematics＜/li＞＜li＞Written by leaders in the field of computational approaches to mind and brain＜/li＞＜/ul＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	8,759円
Bayesian Analysis with Python Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, 2nd Edition【電子書籍】[ Osvaldo Martin ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ＜/li＞＜li＞A modern, practical and computational approach to Bayesian statistical modeling＜/li＞＜li＞A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.＜/p＞＜p＞The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others.＜/p＞＜p＞By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Build probabilistic models using the Python library PyMC3＜/li＞＜li＞Analyze probabilistic models with the help of ArviZ＜/li＞＜li＞Acquire the skills required to sanity check models and modify them if necessary＜/li＞＜li＞Understand the advantages and caveats of hierarchical models＜/li＞＜li＞Find out how different models can be used to answer different data analysis questions＜/li＞＜li＞Compare models and choose between alternative ones＜/li＞＜li＞Discover how different models are unified from a probabilistic perspective＜/li＞＜li＞Think probabilistically and benefit from the flexibility of the Bayesian framework＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	5,155円
Understanding Computational Bayesian Statistics【電子書籍】[ William M. Bolstad ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞A hands-on introduction to computational statistics＜/strong＞＜strong＞from a Bayesian point of view＜/strong＞＜/p＞＜p＞Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, ＜em＞Understanding Computational Bayesian Statistics＜/em＞ successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model, and the proportional hazards model.＜/p＞＜p＞The book begins with an outline of the similarities and differences between Bayesian and the likelihood approaches to statistics. Subsequent chapters present key techniques for using computer software to draw Monte Carlo samples from the incompletely known posterior distribution and performing the Bayesian inference calculated from these samples. Topics of coverage include:＜/p＞＜ul＞＜li＞Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distribution＜/li＞＜li＞The distributions from the one-dimensional exponential family＜/li＞＜li＞Markov chains and their long-run behavior＜/li＞＜li＞The Metropolis-Hastings algorithm＜/li＞＜li＞Gibbs sampling algorithm and methods for speeding up convergence＜/li＞＜li＞Markov chain Monte Carlo sampling＜/li＞＜/ul＞＜p＞Using numerous graphs and diagrams, the author emphasizes a step-by-step approach to computational Bayesian statistics. At each step, important aspects of application are detailed, such as how to choose a prior for logistic regression model, the Poisson regression model, and the proportional hazards model. A related Web site houses R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations, and detailed appendices in the book guide readers through the use of these software packages.＜/p＞＜p＞＜em＞Understanding Computational Bayesian Statistics＜/em＞ is an excellent book for courses on computational statistics at the upper-level undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners who use computer programs to conduct statistical analyses of data and solve problems in their everyday work.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	20,920円
Bayesian Nonparametrics for Causal Inference and Missing Data【電子書籍】[ Michael J. Daniels ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Bayesian Nonparametrics for Causal Inference and Missing Data＜/strong＞ provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest.＜/p＞＜p＞The book is divided into three parts. ＜strong＞Part I＜/strong＞ develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. ＜strong＞Part II＜/strong＞ introduces the fundamental BNP tools required to address causal inference and missing data problems. ＜strong＞Part III＜/strong＞ shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials.＜/p＞＜p＞Features＜/p＞＜p＞? Thorough discussion of both BNP and its interplay with causal inference and missing data＜/p＞＜p＞? How to use BNP and g-computation for causal inference and non-ignorable missingness＜/p＞＜p＞? How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions＜/p＞＜p＞? Detailed case studies illustrating the application of BNP methods to causal inference and missing data＜/p＞＜p＞? R code and/or packages to implement BNP in causal inference and missing data problems＜/p＞＜p＞The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	11,792円
A Student’s Guide to Bayesian Statistics【電子書籍】[ Ben Lambert ] 楽天Kobo電子書籍ストア	＜p＞Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics.＜/p＞＜p＞Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers:＜/p＞＜ul＞＜li＞An introduction to probability and Bayesian inference＜/li＞＜li＞Understanding Bayes′ rule＜/li＞＜li＞Nuts and bolts of Bayesian analytic methods＜/li＞＜li＞Computational Bayes and real-world Bayesian analysis＜/li＞＜li＞Regression analysis and hierarchical methods＜/li＞＜/ul＞＜p＞This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	7,750円
Bayesian Statistics and Marketing【電子書籍】[ Peter E. Rossi ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Fine-tune your marketing research with this cutting-edge statistical toolkit＜/strong＞＜/p＞＜p＞＜em＞Bayesian Statistics and Marketing＜/em＞ illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a comprehensive, accessible overview of this subject essential for any statistically informed marketing researcher or practitioner.＜/p＞＜p＞Economists and other social scientists will find a comprehensive treatment of many Bayesian methods that are central to the problems in social science more generally. This includes a practical approach to computationally challenging problems in random coefficient models, non-parametrics, and the problems of endogeneity.＜/p＞＜p＞Readers of the second edition of ＜em＞Bayesian Statistics and Marketing＜/em＞ will also find:＜/p＞＜ul＞＜li＞Discussion of Bayesian methods in text analysis and Machine Learning＜/li＞＜li＞Updates throughout reflecting the latest research and applications＜/li＞＜li＞Discussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here＜/li＞＜li＞Extensive case studies throughout to link theory and practice＜/li＞＜/ul＞＜p＞＜em＞Bayesian Statistics and Marketing＜/em＞ is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	13,947円
Bayesian Statistics the Fun Way Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks【電子書籍】[ Will Kurt ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples.＜/strong＞＜/p＞＜p＞Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. ＜em＞Bayesian Statistics the Fun Way＜/em＞ will change that.＜/p＞＜p＞This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples.＜/p＞＜p＞By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to:＜/p＞＜p＞- How to measure your own level of uncertainty in a conclusion or belief＜br /＞ - Calculate Bayes theorem and understand what it's useful for＜br /＞ - Find the posterior, likelihood, and prior to check the accuracy of your conclusions＜br /＞ - Calculate distributions to see the range of your data＜br /＞ - Compare hypotheses and draw reliable conclusions from them＜/p＞＜p＞Next time you find yourself with a sheaf of survey results and no idea what to do with them, turn to ＜em＞Bayesian Statistics the Fun Way＜/em＞ to get the most value from your data.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	4,312円
Bayesian Statistical Methods【電子書籍】[ Brian J. Reich ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Bayesian Statistical Methods＜/strong＞ provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.＜/p＞＜p＞In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics:＜/p＞＜ul＞＜li＞＜/li＞＜li＞Advice on selecting prior distributions＜/li＞＜li＞＜/li＞＜li＞Computational methods including Markov chain Monte Carlo (MCMC)＜/li＞＜li＞＜/li＞＜li＞Model-comparison and goodness-of-fit measures, including sensitivity to priors＜/li＞＜li＞＜/li＞＜li＞Frequentist properties of Bayesian methods＜/li＞＜/ul＞＜p＞Case studies covering advanced topics illustrate the flexibility of the Bayesian approach:＜/p＞＜ul＞＜li＞＜/li＞＜li＞Semiparametric regression＜/li＞＜li＞＜/li＞＜li＞Handling of missing data using predictive distributions＜/li＞＜li＞＜/li＞＜li＞Priors for high-dimensional regression models＜/li＞＜li＞＜/li＞＜li＞Computational techniques for large datasets＜/li＞＜li＞＜/li＞＜li＞Spatial data analysis＜/li＞＜/ul＞＜p＞The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website.＜/p＞＜p＞Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the ＜em＞Journal of Agricultural, Biological, and Environmental Statistics＜/em＞ and was awarded the LeRoy & Elva Martin Teaching Award.＜/p＞＜p＞Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	9,602円
Machine Learning A Bayesian and Optimization Perspective【電子書籍】[ Sergios Theodoridis ] 楽天Kobo電子書籍ストア	＜p＞Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models. New to this edition: - Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs). - Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes. - Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method - Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling - Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	11,103円
Bayesian Networks in R with Applications in Systems Biology【電子書籍】[ Radhakrishnan Nagarajan ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Bayesian Networks in R with Applications in Systems Biology＜/strong＞ is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using theapproaches presented in the book.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	9,723円
Introduction to Bayesian Econometrics【電子書籍】[ Edward Greenberg ] 楽天Kobo電子書籍ストア	＜p＞This textbook explains the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It defines the likelihood function, prior distributions and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The new edition also emphasizes the R programming language.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	7,818円
Bayesian Estimation of DSGE Models【電子書籍】[ Edward P. Herbst ] 楽天Kobo電子書籍ストア	＜p＞Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations.＜/p＞＜p＞＜em＞Bayesian Estimation of DSGE Models＜/em＞ is essential reading for graduate students, academic researchers, and practitioners at policy institutions.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	7,413円
Bayesian Statistical Modeling with Stan, R, and Python【電子書籍】[ Kentaro Matsuura ] 楽天Kobo電子書籍ストア	＜p＞This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language.＜/p＞＜p＞The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines.＜/p＞＜p＞Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	18,231円
Bayesian Scientific Computing【電子書籍】[ Daniela Calvetti ] 楽天Kobo電子書籍ストア	＜p＞The once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. This book provides an insider’s view of how to combine two mature fields, scientific computing and Bayesian inference, into a powerful language leveraging the capabilities of both components for computational efficiency, high resolution power and uncertainty quantification ability. The impact of Bayesian scientific computing has been particularly significant in the area of computational inverse problems where the data are often scarce or of low quality, but some characteristics of the unknown solution may be available a priori. The ability to combine the flexibility of the Bayesian probabilistic framework with efficient numerical methods has contributed to the popularity of Bayesian inversion, with the prior distribution being the counterpart of classical regularization. However, the interplay between Bayesian inference and numerical analysis is much richer than providing an alternative way to regularize inverse problems, as demonstrated by the discussion of time dependent problems, iterative methods, and sparsity promoting priors in this book. The quantification of uncertainty in computed solutions and model predictions is another area where Bayesian scientific computing plays a critical role. This book demonstrates that Bayesian inference and scientific computing have much more in common than what one may expect, and gradually builds a natural interface between these two areas.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	15,800円
Bayesian Disease Mapping Hierarchical Modeling in Spatial Epidemiology, Third Edition【電子書籍】[ Andrew B. Lawson ] 楽天Kobo電子書籍ストア	＜p＞Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, ＜strong＞Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition＜/strong＞ provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications.＜/p＞＜p＞In addition to the new material, the book also covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data.＜/p＞＜p＞The target audience for this text is public health specialists, epidemiologists, and biostatisticians who need to work with geo-referenced health data.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	11,287円
Coherent Stress Testing A Bayesian Approach to the Analysis of Financial Stress【電子書籍】[ Riccardo Rebonato ] 楽天Kobo電子書籍ストア	＜p＞In ＜em＞Coherent Stress Testing: A Bayesian Approach＜/em＞, industry expert Riccardo Rebonato presents a groundbreaking new approach to this important but often undervalued part of the risk management toolkit.＜/p＞＜p＞Based on the author's extensive work, research and presentations in the area, the book fills a gap in quantitative risk management by introducing a new and very intuitively appealing approach to stress testing based on expert judgement and Bayesian networks. It constitutes a radical departure from the traditional statistical methodologies based on Economic Capital or Extreme-Value-Theory approaches.＜/p＞＜p＞The book is split into four parts. Part I looks at stress testing and at its role in modern risk management. It discusses the distinctions between risk and uncertainty, the different types of probability that are used in risk management today and for which tasks they are best used. Stress testing is positioned as a bridge between the statistical areas where VaR can be effective and the domain of total Keynesian uncertainty. Part II lays down the quantitative foundations for the concepts described in the rest of the book. Part III takes readers through the application of the tools discussed in part II, and introduces two different systematic approaches to obtaining a coherent stress testing output that can satisfy the needs of industry users and regulators. In part IV the author addresses more practical questions such as embedding the suggestions of the book into a viable governance structure.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	9,412円
Quantum Mechanics and Bayesian Machines【電子書籍】[ George Chapline ] 楽天Kobo電子書籍ストア	＜p＞This compendium brings together the fields of Quantum Computing, Machine Learning, and Neuromorphic Computing. It provides an elementary introduction for students and researchers interested in quantum or neuromorphic computing to the basics of machine learning and the possibilities for using quantum devices for pattern recognition and Bayesian decision tree problems. The volume also highlights some possibly new insights into the meaning of quantum mechanics, for example, why a description of Nature requires probabilistic rather than deterministic methods.＜/p＞＜p＞＜strong＞Contents:＜/strong＞＜/p＞＜ul＞＜li＞＜p＞Preface＜/p＞＜/li＞＜li＞＜p＞About the Author＜/p＞＜/li＞＜li＞＜p＞Acknowledgments＜/p＞＜/li＞＜li＞＜p＞Introduction＜/p＞＜/li＞＜li＞＜p＞＜em＞＜strong＞Six Fundamental Discoveries:＜/strong＞＜/em＞＜/p＞＜ul＞＜li＞Bayes's Probability Formula＜/li＞＜li＞The Wiener and Kalman?Bucy Filters＜/li＞＜li＞Bellman's Dynamic Programming Approach to Optimal Control＜/li＞＜li＞Feynman's Path Integral Approach to Quantum Mechanics＜/li＞＜li＞Quantum Solution of the Traveling Salesman Problem (TSP)＜/li＞＜/ul＞＜/li＞＜li＞＜p＞＜em＞＜strong＞Ockham's Razor:＜/strong＞＜/em＞＜/p＞＜ul＞＜li＞Bayesian Searches＜/li＞＜li＞A Tale of Two Costs＜/li＞＜li＞Hidden Factors and the Helmholtz Machine＜/li＞＜/ul＞＜/li＞＜li＞＜p＞＜em＞＜strong＞Control Theory:＜/strong＞＜/em＞＜/p＞＜ul＞＜li＞The Hamilton?Jacobi?Bellman Equation＜/li＞＜li＞Pontryagin Maximum Principle＜/li＞＜li＞Lie?Poisson Dynamics＜/li＞＜li＞＜em＞H＜/em＞∞ Control＜/li＞＜/ul＞＜/li＞＜li＞＜p＞＜em＞＜strong＞Integrable Systems:＜/strong＞＜/em＞＜/p＞＜ul＞＜li＞RH Solution of the Airy Equation＜/li＞＜li＞The KdV Equation＜/li＞＜li＞Segal?Wilson Construction＜/li＞＜li＞The NLS Equation＜/li＞＜li＞Galois Remembered＜/li＞＜/ul＞＜/li＞＜li＞＜p＞＜em＞＜strong＞Quantum Tools:＜/strong＞＜/em＞＜/p＞＜ul＞＜li＞Weyl Remembered＜/li＞＜li＞Helstrom's Theorem and Universal Hilbert Spaces＜/li＞＜li＞Measurement-based Quantum Computation＜/li＞＜/ul＞＜/li＞＜li＞＜p＞＜em＞＜strong＞Quantum Self-organization:＜/strong＞＜/em＞＜/p＞＜ul＞＜li＞Pontryagin Control and Quantum Criticality＜/li＞＜li＞Quantum Theory of Innovations＜/li＞＜li＞Quantum Helmholtz Machine＜/li＞＜li＞Ad Mammalian Intelligence＜/li＞＜/ul＞＜/li＞＜li＞＜p＞＜em＞＜strong＞Holistic Computing:＜/strong＞＜/em＞＜/p＞＜ul＞＜li＞Quantum Mechanics and 3D Geometry＜/li＞＜li＞Cognitive Science and Quantum Physics＜/li＞＜/ul＞＜/li＞＜li＞＜p＞＜em＞＜strong＞Appendices:＜/strong＞＜/em＞＜/p＞＜ul＞＜li＞Gaussian Processes＜/li＞＜li＞Wiener?Hopf Methods＜/li＞＜li＞Riemann Surfaces＜/li＞＜li＞The Eightfold Way＜/li＞＜li＞Quantum Theory of Brownian Motion＜/li＞＜/ul＞＜/li＞＜li＞＜p＞References＜/p＞＜/li＞＜li＞＜p＞Index＜/p＞＜/li＞＜/ul＞＜p＞＜strong＞Readership:＜/strong＞ Researchers, academics, professionals and graduate students in pattern recognition/image analysis, machine learning, quantum mechanics and general applied maths.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	8,879円
Statistical Rethinking A Bayesian Course with Examples in R and STAN【電子書籍】[ Richard McElreath ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Winner of the 2024 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)＜/strong＞＜/p＞＜p＞＜strong＞Statistical Rethinking: A Bayesian Course with Examples in R and Stan＜/strong＞ builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.＜/p＞＜p＞The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.＜/p＞＜p＞The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.＜/p＞＜p＞＜strong＞Features＜/strong＞＜/p＞＜ul＞＜li＞＜p＞Integrates working code into the main text.＜/p＞＜/li＞＜li＞＜p＞Illustrates concepts through worked data analysis examples.＜/p＞＜/li＞＜li＞＜p＞Emphasizes understanding assumptions and how assumptions are reflected in code.＜/p＞＜/li＞＜li＞＜p＞Offers more detailed explanations of the mathematics in optional sections.＜/p＞＜/li＞＜li＞＜p＞Presents examples of using the dagitty R package to analyze causal graphs.＜/p＞＜p＞Provides the rethinking R package on the author's website and on GitHub.＜/p＞＜/li＞＜/ul＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	18,534円
Bayesian Data Analysis【電子書籍】[ Andrew Gelman ] 楽天Kobo電子書籍ストア	＜p＞Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	18,534円
Enhancing Deep Learning with Bayesian Inference Create more powerful, robust deep learning systems with Bayesian deep learning in Python【電子書籍】[ Matt Benatan ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Develop Bayesian Deep Learning models to help make your own applications more robust.＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Gain insights into the limitations of typical neural networks＜/li＞＜li＞Acquire the skill to cultivate neural networks capable of estimating uncertainty＜/li＞＜li＞Discover how to leverage uncertainty to develop more robust machine learning systems＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Deep learning has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put, typical deep learning methods do not know when they don’t know. The field of Bayesian Deep Learning contains a range of methods for approximate Bayesian inference with deep networks. These methods help to improve the robustness of deep learning systems as they tell us how confident they are in their predictions, allowing us to take more in how we incorporate model predictions within our applications. Through this book, you will be introduced to the rapidly growing field of uncertainty-aware deep learning, developing an understanding of the importance of uncertainty estimation in robust machine learning systems. You will learn about a variety of popular Bayesian Deep Learning methods, and how to implement these through practical Python examples covering a range of application scenarios. By the end of the book, you will have a good understanding of Bayesian Deep Learning and its advantages, and you will be able to develop Bayesian Deep Learning models for safer, more robust deep learning systems.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Understand advantages and disadvantages of Bayesian inference and deep learning＜/li＞＜li＞Understand the fundamentals of Bayesian Neural Networks＜/li＞＜li＞Understand the differences between key BNN implementations/approximations＜/li＞＜li＞Understand the advantages of probabilistic DNNs in production contexts＜/li＞＜li＞How to implement a variety of BDL methods in Python code＜/li＞＜li＞How to apply BDL methods to real-world problems＜/li＞＜li＞Understand how to evaluate BDL methods and choose the best method for a given task＜/li＞＜li＞Learn how to deal with unexpected data in real-world deep learning applications＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. You’re expected to have a solid understanding of the fundamentals of machine learning and probability, along with prior experience working with machine learning and deep learning models.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	5,166円
Fundamentals of Bayesian Epistemology 1 Introducing Credences【電子書籍】[ Michael G. Titelbaum ] 楽天Kobo電子書籍ストア	＜p＞Bayesian ideas have recently been applied across such diverse fields as philosophy, statistics, economics, psychology, artificial intelligence, and legal theory. Fundamentals of Bayesian Epistemology examines epistemologists' use of Bayesian probability mathematics to represent degrees of belief. Michael G. Titelbaum provides an accessible introduction to the key concepts and principles of the Bayesian formalism, enabling the reader both to follow epistemological debates and to see broader implications Volume 1 begins by motivating the use of degrees of belief in epistemology. It then introduces, explains, and applies the five core Bayesian normative rules: Kolmogorov's three probability axioms, the Ratio Formula for conditional degrees of belief, and Conditionalization for updating attitudes over time. Finally, it discusses further normative rules (such as the Principal Principle, or indifference principles) that have been proposed to supplement or replace the core five. Volume 2 gives arguments for the five core rules introduced in Volume 1, then considers challenges to Bayesian epistemology. It begins by detailing Bayesianism's successful applications to confirmation and decision theory. Then it describes three types of arguments for Bayesian rules, based on representation theorems, Dutch Books, and accuracy measures. Finally, it takes on objections to the Bayesian approach and alternative formalisms, including the statistical approaches of frequentism and likelihoodism.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	3,722円
Bayesian Regression Modeling with INLA【電子書籍】[ Xiaofeng Wang ] 楽天Kobo電子書籍ストア	＜p＞INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference.＜/p＞＜p＞Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download.＜/p＞＜p＞The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work.＜/p＞＜p＞＜strong＞Xiaofeng Wang＜/strong＞ is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic.＜/p＞＜p＞＜strong＞Yu Ryan Yue＜/strong＞ is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York.＜/p＞＜p＞＜strong＞Julian J. Faraway＜/strong＞ is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	10,949円
Bayesian Decision Networks Fundamentals and Applications【電子書籍】[ Fouad Sabry ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞What Is Bayesian Decision Networks＜/strong＞＜/p＞＜p＞A Bayesian network is a probabilistic graphical model that depicts a set of variables and their conditional relationships via a directed acyclic graph (DAG). In other words, a Bayesian network is a type of directed acyclic graph. Bayesian networks are perfect for determining the likelihood that any one of multiple possible known causes was the contributing factor in an event that has already taken place and making a prediction based on that likelihood. For instance, the probabilistic links that exist between diseases and symptoms might be represented by a Bayesian network. The network may be used to compute the odds of the presence of a variety of diseases based on the symptoms that are provided.＜/p＞＜p＞＜strong＞How You Will Benefit＜/strong＞＜/p＞＜p＞(I) Insights, and validations about the following topics:＜/p＞＜p＞Chapter 1: Bayesian network＜/p＞＜p＞Chapter 2: Influence diagram＜/p＞＜p＞Chapter 3: Graphical model＜/p＞＜p＞Chapter 4: Hidden Markov model＜/p＞＜p＞Chapter 5: Decision tree＜/p＞＜p＞Chapter 6: Gibbs sampling＜/p＞＜p＞Chapter 7: Decision analysis＜/p＞＜p＞Chapter 8: Value of information＜/p＞＜p＞Chapter 9: Probabilistic forecasting＜/p＞＜p＞Chapter 10: Causal graph＜/p＞＜p＞(II) Answering the public top questions about bayesian decision networks.＜/p＞＜p＞(III) Real world examples for the usage of bayesian decision networks in many fields.＜/p＞＜p＞(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of bayesian decision networks' technologies.＜/p＞＜p＞＜strong＞Who This Book Is For＜/strong＞＜/p＞＜p＞Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of bayesian decision networks.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	400円
Probabilistic Risk Analysis and Bayesian Decision Theory【電子書籍】[ Marcel van Oijen ] 楽天Kobo電子書籍ストア	＜p＞The book shows how risk, defined as the statistical expectation of loss, can be formally decomposed as the product of two terms: hazard probability and system vulnerability. This requires a specific definition of vulnerability that replaces the many fuzzy definitions abounding in the literature. The approach is expanded to more complex risk analysis with three components rather than two, and with various definitions of hazard. Equations are derived to quantify the uncertainty of each risk component and show how the approach relates to Bayesian decision theory. Intended for statisticians, environmental scientists and risk analysts interested in the theory and application of risk analysis, this book provides precise definitions, new theory, and many examples with full computer code. The approach is based on straightforward use of probability theory which brings rigour and clarity. Only a moderate knowledge and understanding of probability theory is expected from the reader.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	6,685円
Bayesian Optimization for Materials Science【電子書籍】[ Daniel Packwood ] 楽天Kobo電子書籍ストア	＜p＞This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science.＜/p＞＜p＞Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While research in these directions has been reported in high-profile journals, until now there has been no textbook aimed specifically at materials scientists who wish to incorporate Bayesian optimization into their own research. This book will be accessible to researchers and students in materials science who have a basic background in calculus and linear algebra.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	7,900円
Bayesian Methods in Finance Probabilistic Approaches to Market Uncertainty【電子書籍】[ William Johnson ] 楽天Kobo電子書籍ストア	＜p＞"Bayesian Methods in Finance: Probabilistic Approaches to Market Uncertainty" offers an authoritative exploration of how Bayesian statistics can transform financial analysis into a more predictive and adaptive process. Within the rapidly evolving tapestry of global financial markets, the ability to quantify uncertainty and integrate diverse streams of information stands as a crucial advantage. This book expertly demystifies the intricate principles of Bayesian thinking, guiding readers through its application across a spectrum of financial contexts, from asset pricing to risk management and portfolio construction. Through a careful blend of theory and practical insights, it introduces the reader to Bayesian frameworks that eclipse traditional models in both flexibility and robustness, making them indispensable tools for modern investors and financial professionals.＜/p＞＜p＞Readers will find a clear roadmap for navigating the complex landscape of market dynamics with the confidence that comes from sound, data-driven strategies. By integrating Bayesian approaches with machine learning, this text unlocks more nuanced analyses and predictive capabilities, catering to both novice learners and experienced market strategists. Rich with real-world case studies, each chapter not only illuminates techniques but also showcases their powerful applications in decision-making processes. Embark on a deep dive into the future of financial modeling, where the calculated embrace of uncertainty opens doors to innovative solutions and unparalleled insights.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	1,493円
Modern Bayesian Statistics in Clinical Research【電子書籍】[ Ton J. Cleophas ] 楽天Kobo電子書籍ストア	＜p＞The current textbook has been written as a help to medical / health professionals and students for the study of modern Bayesian statistics, where posterior and prior odds have been replaced with posterior and prior likelihood distributions. Why may likelihood distributions better than normal distributions estimate uncertainties of statistical test results? Nobody knows for sure, and the use of likelihood distributions instead of normal distributions for the purpose has only just begun, but already everybody is trying and using them. SPSS statistical software version 25 (2017) has started to provide a combined module entitled Bayesian Statistics including almost all of the modern Bayesian tests (Bayesian t-tests, analysis of variance (anova), linear regression, crosstabs etc.).＜/p＞＜p＞Modern Bayesian statistics is based on biological likelihoods, and may better fit clinical data than traditional tests based normal distributions do. This is the first edition to systematically implymodern Bayesian statistics in traditional clinical data analysis. This edition also demonstrates that Markov Chain Monte Carlo procedures laid out as Bayesian tests provide more robust correlation coefficients than traditional tests do. It also shows that traditional path statistics are both textually and conceptionally like Bayes theorems, and that structural equations models computed from them are the basis of multistep regressions, as used with causal Bayesian networks.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	7,900円
Bayesian Biostatistics【電子書籍】[ Donald A. Berry ] 楽天Kobo電子書籍ストア	＜p＞This work provides descriptions, explanations and examples of the Bayesian approach to statistics, demonstrating the utility of Bayesian methods for analyzing real-world problems in the health sciences. The work considers the individual components of Bayesian analysis.;College or university bookstores may order five or more copies at a special student price, available on request from Marcel Dekker, Inc.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	14,320円
Bayesian Population Analysis using WinBUGS A Hierarchical Perspective【電子書籍】[ Michael Schaub ] 楽天Kobo電子書籍ストア	＜p＞Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. - Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist - All WinBUGS/OpenBUGS analyses are completely integrated in software R - Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	7,714円