Dynamic Programming and Bayesian Inference: Concepts and Applications
by Mohammad Saber Fallah Nezhad (ed.)
Publisher: InTech 2014
ISBN-13: 9789535113645
Number of pages: 164
Description:
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.
Download or read it online for free here:
Download link
(multiple PDF files)
Similar books
Applied Statistics
by Mohammed A. Shayib - Bookboon
The book introduces the concepts, definitions, and terminology of the subject in an elementary presentation with a mathematical background which does not surpass college algebra. It should prepare the reader to make a good decision based on data.
(3734 views)
by Mohammed A. Shayib - Bookboon
The book introduces the concepts, definitions, and terminology of the subject in an elementary presentation with a mathematical background which does not surpass college algebra. It should prepare the reader to make a good decision based on data.
(3734 views)
Statistics
- Wikibooks
Statistics is used in almost every field of research. We will learn about subjects in modern statistics and some applications of statistics. We will also lay out some of the background mathematical concepts required to begin studying statistics.
(5712 views)
- Wikibooks
Statistics is used in almost every field of research. We will learn about subjects in modern statistics and some applications of statistics. We will also lay out some of the background mathematical concepts required to begin studying statistics.
(5712 views)
Think Bayes: Bayesian Statistics Made Simple
by Allen B. Downey - Green Tea Press
Think Bayes is an introduction to Bayesian statistics using computational methods. Contents: Bayes's Theorem; Computational statistics; Tanks and Trains; Urns and Coins; Odds and addends; Hockey; The variability hypothesis; Hypothesis testing.
(5473 views)
by Allen B. Downey - Green Tea Press
Think Bayes is an introduction to Bayesian statistics using computational methods. Contents: Bayes's Theorem; Computational statistics; Tanks and Trains; Urns and Coins; Odds and addends; Hockey; The variability hypothesis; Hypothesis testing.
(5473 views)
Everything you wanted to know about Data Analysis and Fitting
by Peter Young - arXiv
These notes discuss, in a style intended for physicists, how to average data and fit it to some functional form. I try to make clear what is being calculated, what assumptions are being made, and to give a derivation of results.
(5232 views)
by Peter Young - arXiv
These notes discuss, in a style intended for physicists, how to average data and fit it to some functional form. I try to make clear what is being calculated, what assumptions are being made, and to give a derivation of results.
(5232 views)