Computational Bayesian Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computational Bayesian Statistics write by M. Antónia Amaral Turkman. This book was released on 2019-02-28. Computational Bayesian Statistics available in PDF, EPUB and Kindle. This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods.
Bayesian Core: A Practical Approach to Computational Bayesian Statistics
Bayesian Core: A Practical Approach to Computational Bayesian Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Core: A Practical Approach to Computational Bayesian Statistics write by Jean-Michel Marin. This book was released on 2007-05-26. Bayesian Core: A Practical Approach to Computational Bayesian Statistics available in PDF, EPUB and Kindle. This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book.
Understanding Computational Bayesian Statistics
Understanding Computational Bayesian Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Understanding Computational Bayesian Statistics write by William M. Bolstad. This book was released on 2011-09-20. Understanding Computational Bayesian Statistics available in PDF, EPUB and Kindle. A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics 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. 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: Direct ways to draw a random sample from the posterior by reshaping a random sample drawn from an easily sampled starting distribution The distributions from the one-dimensional exponential family Markov chains and their long-run behavior The Metropolis-Hastings algorithm Gibbs sampling algorithm and methods for speeding up convergence Markov chain Monte Carlo sampling 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. Understanding Computational Bayesian Statistics 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.
Bayesian Modeling and Computation in Python
Bayesian Modeling and Computation in Python - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Modeling and Computation in Python write by Osvaldo A. Martin. This book was released on 2021-12-28. Bayesian Modeling and Computation in Python available in PDF, EPUB and Kindle. Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the internals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics. This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.
Computational Bayesian Statistics
Computational Bayesian Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computational Bayesian Statistics write by M. Antónia Amaral Turkman. This book was released on 2019-02-28. Computational Bayesian Statistics available in PDF, EPUB and Kindle. Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.
