ABSTRACT

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie

part I|336 pages

Foundations, Methodology, and Algorithms

chapter 3|26 pages

Reversible Jump MCMC

chapter 5|50 pages

MCMC Using Hamiltonian Dynamics

chapter 9|26 pages

Spatial Point Processes

chapter 12|24 pages

Likelihood-Free MCMC

part II|238 pages

Part II. Applications and Case Studies

chapter 17|30 pages

Statistical Ecology

chapter 21|18 pages

MCMC for State–Space Models