Bayesian Methods for Statistical Analysis
by Borek Puza
Publisher: ANU Press 2015
Number of pages: 697
A book on statistical methods for analysing a wide variety of data. Topics: bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code.
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by Douglas McNair (ed.) - IntechOpen
Bayesian networks have recently experienced increased interest and diverse applications in numerous areas, including economics, risk analysis, assets and liabilities management, AI and robotics, transportation systems planning and optimization, etc.
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.
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.
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.