by James E. Gentle
Publisher: Springer 2009
Number of pages: 729
This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics.
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by Ivan Lowe - scientificlanguage.com
Here I present statistics for the ordinary person. Examples are taken from ordinary life. The book begins with basic concepts behind the statistics and never gets harder than simple arithmetic. The course is presented as a series of key ideas.
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 Mohammad Saber Fallah Nezhad (ed.) - InTech
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.
by Ryan Martin - University of Illinois at Chicago
Table of contents: Statistics and Sampling Distributions; Point Estimation Basics; Likelihood and Maximum Likelihood Estimation; Sufficiency and Minimum Variance Estimation; Hypothesis Testing; Bayesian Statistic; What Else is There to Learn?