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Writing reversible jump MCMC in NIMBLE

February 15, 2017
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Writing reversible jump MCMC in NIMBLE

Writing reversible jump MCMC samplers in NIMBLE Writing reversible jump MCMC samplers in NIMBLE Introduction Reversible jump Markov chain Monte Carlo (RJMCMC) is a powerful method for drawing posterior samples over multiple models by jumping between models as part of the sampling. For a simple example that I’ll use below, think about a regression model

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Building Particle Filters and Particle MCMC in NIMBLE

January 9, 2017
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Building Particle Filters and Particle MCMC in NIMBLE

An Example of Using nimble‘s Particle Filtering Algorithms This example shows how to construct and conduct inference on a state space model using particle filtering algorithms. nimble currently has versions of the bootstrap filter, the auxiliary particle filter, the ensemble Kalman filter, and the Liu and West filter implemented. Additionally, particle MCMC samplers are available

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NIMBLE package for hierarchical modeling (MCMC and more) faster and more flexible in version 0.6-1

October 31, 2016
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NIMBLE version 0.6-1 has been released on CRAN and at  r-nimble.org.   NIMBLE is a system that allows you to: Write general hierarchical statistical models in BUGS code and create a corresponding model object to use in R. Build Markov chain Monte Carlo (MCMC), particle filters, Monte Carlo Expectation Maximization (MCEM), or write generic algorithms

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NIMBLE: A new way to do MCMC (and more) from BUGS code in R

April 8, 2016
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NIMBLE: A new way to do MCMC (and more) from BUGS code in R

Yesterday we released version 0.5 of NIMBLE on our web site, r-nimble.org. (We’ll get it onto CRAN soon, but it has some special needs to work out.) NIMBLE tries to fill a gap in what R programmers and analysts can do with general hierarchical models. Packages like WinBUGS, OpenBUGS, JAGS and Stan provide a language

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