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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(multiple PDF files)
by Alex Reinhart - refsmmat.com
This is a guide to the most popular statistical errors and slip-ups committed by scientists every day, in the lab and in peer-reviewed journals. It assumes no prior knowledge of statistics, you can read it before your first statistics course.
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 Michael Lavine
Upper undergraduate or graduate book in statistical thinking for students with a background in calculus and the ability to think abstractly. The focus is on ideas and concepts, as opposed to technical details of how to put those ideas into practice.
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.