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Introduction to Bayesian Estimation and Copula Models of Dependence by Arkady Shemyakin; Alexander Kniazev, (Mathematician) is a mathematics available to read on EtoBox.

What is Introduction to Bayesian Estimation and Copula Models of Dependence about?

Presents an introduction to Bayesian statistics, presents an emphasis on Bayesian methods (prior and posterior), Bayes estimation, prediction, MCMC,Bayesian regression, and Bayesian analysis of statistical modelsof dependence, and features a focus on copulas for risk management Introduction to Bayesian Estimation and Copula Models of Dependence emphasizes the applications of Bayesian analysis to copula modeling and equips readers with the tools needed to implement the procedures of Bayesian estimation in copula models of dependence. This book is structured in two parts: the first four chapters serve as a general introduction to Bayesian statistics with a clear emphasis on parametric estimation and the following four chapters stress statistical models of dependence with a focus of copulas. A review of the main concepts is discussed along with the basics of Bayesian statistics including prior information and experimental data, prior and posterior distributions, with an emphasis on Bayesian parametric estimation. The basic mathematical background of both Markov chains and Monte Carlo integration and simulation is also provided. The authors discuss statistical models of dependence with

Who reads Introduction to Bayesian Estimation and Copula Models of Dependence?

Author

Arkady Shemyakin; Alexander Kniazev, (Mathematician)

Publisher

John Wiley & Sons, Incorporated

Published

2017

Language

EN

ISBN

9781118959015

Category

mathematics

Subjects

Mathematics, Stem

Rating

3.9 / 5 (6 ratings)

Updated

2026-03-14