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 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.
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.
by Keijo Ruohonen - Tampere University of Technology
Table of contents: Fundamental sampling distributions and data descriptions; One- and two-sample estimation; Tests of hypotheses; X2-tests; Maximum likelihood estimation; Multiple linear regression; Nonparametric statistics; Stochastic simulation.
by Douglas S. Shafer, Zhiyi Zhang - lardbucket.org
This book is meant to be a textbook for a standard one-semester introductory statistics course for general education students. Our motivation for writing it is to provide a low-cost alternative to many existing popular textbooks on the market.