Bayesian&Monte Carlo

I have collected several links of monte carlo (MCMC,HMC,SMC,etc)  software and code. If you know others, please let me know in your comment. Thanks.

       BiiPS is a general software for Bayesian inference with interacting particle systems, a.k.a. sequential Monte Carlo (SMC) methods

Popular BayRResRian Software use Gibbs Sampler Algorithm.

Another BUGS Software.

  • JAGS (Just Another Gibbs Sampler)

It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation

New Bayesian Software use Hamiltonian Monte Carlo

Other packages that use the BUGS language are only for Markov chain Monte Carlo (MCMC). With NIMBLE, you can turn BUGS code into model objects and use them for whatever algorithm you want. That includes algorithms provided with NIMBLE and algorithms you write using nimbleFunctions. NIMBLE extends BUGS by allowing multiple parameterizations for distributions, user-written functions and distributions, and more.

Mamba is an open platform for the implementation and application of MCMC methods to perform Bayesian analysis in julia. The package provides a framework for (1) specification of hierarchical models through stated relationships between data, parameters, and statistical distributions; (2) block-updating of parameters with samplers provided, defined by the user, or available from other packages; (3) execution of sampling schemes; and (4) posterior inference. It is intended to give users access to all levels of the design and implementation of MCMC simulators to particularly aid in the development of new methods.

MCMCpack is a R package designed to allow users to perform Bayesian inference via Markov chain Monte Carlo (MCMC)

This is Matlab version of FBM (Software for Flexible Bayesian Modeling and Markov Chain Sampling) originaly written by Radford Neal.  This matlab version is first implemented by Simo Särkkä and Later Aki Vehtari added additional functions, and fixed many bugs and documentation.

MCMCstuff toolbox is a collection of Matlab functions for Bayesian inference with Markov chain Monte Carlo (MCMC) methods

This page contains some Monte Carlo code for Dirichlet Process.

Software for his research (Neural Network , Gaussian Mixture, etc)

Software that implement his research.

This page contais any Package that raftery helped to develop it.

This toolbox provides tools to generate and analyse Metropolis-Hastings MCMC chain using multivariate Gaussian proposal distribution

This package is an R implementation of the Hybrid Monte Carlo and Multipoint Hybrid Monte Carlo sampling techniques described in Liu (2001): “Monte Carlo Strategies in Computing”

Interface to the UNU.RAN library for Universal Non-Uniform RANdom variate generators. Thus it allows to build non-uniform random number generators from quite arbitrary distributions. In particular, it provides an algorithm for fast numerical inversion for distribution with given density function. In addition, the package contains densities, distribution functions and quantiles from a couple of distributions.

Convenience functions for multivariate MCMC using univariate samplers including: slice sampler with stepout and shrinkage (Neal, 2003), adaptive rejection sampler (Gilks and Wild, 1992), adaptive rejection Metropolis (Gilks et al 1995), and univariate Metropolis with Gaussian proposal.

This package provides R with access to the Sequential Monte Carlo Template Classes by Johansen (Journal of Statistical Software, 2009, v30, i6). At present, two additional examples have been added, and the first example from the JSS paper has been extended. Further integration and extensions are planned.

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