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.
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by David W. Stockburger - Missouri State University
This e-book is a complete interactive study guide with quizzing functionality that reports to the instructor. The on-line text also has animated figures and graphs that bring the print graphic to life for deeper understanding.
by J.K. Lindsey - Hodder Education Publishers
Written by a renowned statistician, this book presents the basic ideas behind the statistical methods commonly used in studies of human subjects. It is an ideal guide for advanced undergraduates who are beginning to do their own research.
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.
by Daniel McFadden - University of California, Berkeley
The contents: Economic Analysis and Econometrics; Analysis and Linear Algebra in a Nutshell; Probability Theory in a Nutshell; Limit Theorems in Statistics; Experiments, Sampling, and Statistical Decisions; Estimation; Hypothesis Testing.