Dynamic Programming and Bayesian Inference: Concepts and Applications
by Mohammad Saber Fallah Nezhad (ed.)
Publisher: InTech 2014
Number of pages: 164
Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. The purpose of this volume is to provide some applications of Bayesian optimization and dynamic programming.
Home page url
Download or read it online for free here:
(multiple PDF files)
by David W. Stockburger - Missouri State University
The book for a course in multivariate statistics for first year graduate or advanced undergraduates. It is neither a mathematical treatise nor a cookbook. Instead of complicated mathematical proofs the author wrote about mathematical ideas.
by Douglas McNair (ed.) - IntechOpen
Bayesian networks have recently experienced increased interest and diverse applications in numerous areas, including economics, risk analysis, assets and liabilities management, AI and robotics, transportation systems planning and optimization, etc.
by Joseph B. Kadane - Chapman and Hall/CRC
An accessible, comprehensive guide to the theory of Bayesian statistics, this book presents the subjective Bayesian approach, which has played a pivotal role in game theory, economics, and the recent boom in Markov Chain Monte Carlo methods.
by Christian Akrong Hesse - ResearchGate GmbH
The purpose of this book is to acquaint the reader with the increasing number of applications of statistics in engineering and the applied sciences. Our goal is to introduce the basic theory without getting too involved in mathematical detail.