Partially collapsed Gibbs sampling & path-adaptive Metropolis-Hastings in high-energy astrophysics
نویسندگان
چکیده
As the many examples in this book illustrate, Markov chain Monte Carlo (MCMC) methods have revolutionized Bayesian statistical analyses. Rather than using off-the-shelf models and methods, we can use MCMC to fit application specific models that are designed to account for the particular complexities of a problem at hand. These complex multilevel models are becoming more prevalent throughout the natural, social, and engineering sciences largely because of the ease of using standard MCMC methods such as the Gibbs and Metropolis-Hastings (MH) samplers. Indeed, the ability to easily fit statistical models that directly represent the complexity of a data generation mechanism has arguably lead to the increased 1
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