nimble: MCMC, Particle Filtering, and Programmable Hierarchical Modeling

Citations Over Time

Abstract

A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, deterministic nested approximations, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.

Related Papers

• → On MCMC-Based particle methods for Bayesian filtering: Application to multitarget tracking(2009)62 cited
• → The Alive Particle Filter and Its Use in Particle Markov Chain Monte Carlo(2015)33 cited
• → An MCMC-based particle filter for multiple person tracking(2008)4 cited
• → Acceptance probability of IP-MCMC-PF: revisited(2015)2 cited
• → Channel Tracking for Relay Networks via Adaptive Particle MCMC(2010)6 cited