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 Stan Brown - BrownMath.com
This book is an alternative to the usual textbooks for a one-semester course in statistics. The author tried to make statistics approachable to anyone with high-school math, but it's still a technical subject. There is very little use of formulas.
by Jamie DeCoster - University of Alabama
It is important to know how to understand statistics so that we can make the proper judgments when a person presents us with an argument backed by data. Data are numbers with a context. We must always keep the meaning of our data in mind.
by Philip B. Stark - University of California, Berkeley
This text was written for an introductory class in Statistics for students in Business, Economics, or Social Science. This is the first and last class in Statistics. It also covers logic and reasoning at a level suitable for a general course.
by David Lane - Rice University
This is a resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab.