With the help of the R programming language, this book offers a self-contained introduction to computational Bayesian statistics. This free programming book, which primarily focuses on Bayes factors supported by data sets, has a practical relevance and applicability focus...

The edited book Computational and Numerical Simulations has 20 chapters. The book discusses contemporary work on numerical simulations of engineering and physical systems. 

Numerous stochastic and deterministic problems that are difficult to solve analytically can be solved using Monte Carlo methods.

From the fundamental ideas of statistical inference to sophisticated applications of analyses, this special guide will assist students in building the statistical confidence and expertise necessary to use the Bayesian formula.

This hands-on introduction is intended for researchers who want to use the BayesiaLab software platform to apply Bayesian Networks to their work.

The most common computational problems encountered in Bayesian statistics are presented in this monograph, with a particular emphasis on mixture estimation and model selection issues.

This book shows how a probability distribution of potential MH370 flight trajectories was generated using nonlinear/non-Gaussian Bayesian time series estimate techniques.

Students taking an introductory statistics course should take Foundations in Statistical Reasoning. The focus of the entire book is on how to make decisions based on scant information.

An introduction to Kalman and Bayesian filters is provided in this book. To enable you to run and edit the code in your browser, the book was built using Jupyter Notebook, and all code is written in Python. What...

This famous work is hailed for its understandable, useful approach to data analysis and problem resolution, and is widely regarded as the main literature on Bayesian approaches.