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> Hamiltonian Monte Carlo or Hybrid Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm. Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereb…
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> PyMC3 is a probabilistic programming module for Python that allows users to fit Bayesian models using a variety of numerical methods, most notably Markov chain Monte Carlo (MCMC) and variational inf…
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Hi JOSS,
I developed a package for model calibration called BiPyMc: https://github.com/wgurecky/bipymc. BiPyMc (Bayesian Inference for Python using Markov Chain Monte Carlo) contains implementatio…
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> MC3 is a powerful Bayesian-statistics tool that offers:
> * Levenberg-Marquardt least-squares optimization.
> * Markov-chain Monte Carlo (MCMC) posterior-distribution sampling following the:
> * …
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> bmcmc is a general purpose mcmc package which should be useful for Bayesian data analysis. It uses an adaptive scheme for automatic tuning of proposal distributions. It can also handle hierarchical …
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> kombine is an ensemble sampler built for efficiently exploring multimodal distributions. By using estimates of ensemble’s instantaneous distribution as a proposal, it achieves very fast burnin, foll…
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**Submitting author:** @oaefbsc (Oscar Alejandro Esquivel-Flores)
**Repository:** https://github.com/oaefbsc/Juliacon2019
**Editor:** @vchuravy
**Reviewers:** @dpsanders, @andreasnoack
**Author ins…
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## Description
There needs to be a way to randomly select and run a single `Updater` from a set of eligible ones to execute in a given time step (e.g. `IntegratorHPMCMono`, or `UpdaterBoxMC`, `Upda…
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Hi guys,
I been pondering for the last few days how to make a change in the source to allow for the MALA sampling algorithm to be used along with the Dual Adaptive step size algorithm with a transf…
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> Nested Sampling is a computational approach for integrating posterior probability in order to compare models in Bayesian statistics. It is similar to Markov Chain Monte Carlo (MCMC) in that it gener…