Julia
Bayesian data analysis
Bayesianデータ分析は、データから不確実性を理解し、推論を行うための強力な手法です。Juliaは、その高いパフォーマンスと簡潔な文法により、Bayesian分析を行うのに最適なプログラミング言語の一つです。Juliaのパッケージ（特にTuring.jlやStan.jl）を利用することで、複雑なモデルの構築やサンプリングが容易に行えます。 初学者にとって、Bayesian分析は直感的でありながら深い洞察を与えてくれます。事前分布を設定し、データに基づいてアップデートする過程は、データサイエンスにおける考え方を根本から変えてくれるでしょう。Juliaのシンプルな構文は、数学的なコンセプトを素早く実装する手助けをしてくれます。 さらに、Juliaは多くの強力なビジュアライゼーションライブラリと組み合わせて使うことができ、結果を直感的に理解することが可能です。Bayesian統計の基礎を学ぶことで、より柔軟で適応的なデータ解析が行えるようになります。この旅を通じて、データの背後にあるストーリーを見つける楽しさを存分に味わってください。Bayesian分析を学ぶことで、あなたのデータサイエンススキルが新しいステージへと進化することでしょう。
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JuliaのTuringでベイズモデリング - 導入編
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- 更新日
- 2024-07-15
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| 内容 | Now 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 approach to analysis using up-to-date Bayesian methods. The authors-all leaders in the statistics community-introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book's web page. |
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| 目次 | FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter. |
| 著者 | Dunson,DavidB.／著 Gelman,Andrew／著 Rubin,DonaldB.／著 ほか |
| 出版日 | c2014-01 |
| 出版社 | CRC Press |
| ISBN-13 | 9781439840955 |
| データ提供元 | openBD |