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.
Home page url
Download or read it online for free here:
(multiple PDF files)
by Frederic Barbaresco, Frank Nielsen (eds) - MDPI AG
Contents: Geometric Thermodynamics of Jean-Marie Souriau; Koszul-Vinberg Model of Hessian Information Geometry; Divergence Geometry and Information Geometry; Density of Probability on manifold and metric space; Statistics on Paths and Manifolds; etc.
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.
by Joseph B. Kadane - Chapman and Hall/CRC
An accessible, comprehensive guide to the theory of Bayesian statistics, this book presents the subjective Bayesian approach, which has played a pivotal role in game theory, economics, and the recent boom in Markov Chain Monte Carlo methods.
by Barbara Illowsky, Susan Dean - Illowsky Publising
Intended for introductory statistics courses for students at two and four-year colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite. The book focuses on applications rather than the theory.