A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material.
Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves.
The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material.
Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves.
The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
This book walks you through learning probability and statistics from a Bayesian point of view. From an introduction to probability theory through to frameworks for doing rigorous calculations of...
Bayesian Statistical Methods 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...
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous...
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it.This work is in the "public domain in the United States of...
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