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MCMC Extensibility: New MCMC samplers in NIMBLE

Perry De Valpine Co-Author
UC Berkeley, Environmental Science, Policy & Management
 
Christopher Paciorek Co-Author
University of California, Berkeley
 
Daniel Turek Speaker
Lafayette College
 
Thursday, Aug 7: 9:15 AM - 9:35 AM
Topic-Contributed Paper Session 
Music City Center 
The nimble R package offers a Markov chain Monte Carlo (MCMC) engine, which is capable of operating on generically-specified hierarchical statistical models written using the BUGS language. Here, we focus on the extensibility of nimble's MCMC system, as we describe how new MCMC samplers can be written, and readily incorporated into the MCMC algorithm. We demonstrate how users can author their own MCMC samplers, or readily modify the preexisting sampling algorithms provided with nimble. We also present several recent additions to nimble's library of sampling algorithms, including the gradient-based Hamiltonian Monte Carlo (HMC) and Barker proposal samplers.

Keywords

Markov chain Monte Carlo

nimble

MCMC

Bayesian Statistics