Markov Chain Monte Carlo (MCMC) methods are simply a class of algorithms that use Markov Chains to sample from a particular probability distribution (the Monte Carlo part). They work by creating a Markov Chain where the limiting distribution (or stationary distribution) is simply the distribution we want to sample. Jan 09, 2020 · In this second post of Tweag's four-part series, we discuss Gibbs sampling, an important MCMC-related algorithm which can be advantageous when sampling from multivariate distributions. Two different examples and, again, an interactive Python notebook illustrate use cases and the issue of heavily correlated samples.

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    Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. It was designed with these key principles:

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    Jan 28, 2016 · It features next-generation Markov chain Monte Carlo (MCMC) sampling algorithms such as the No-U-Turn Sampler (NUTS) (Hoffman & Gelman, 2014), a self-tuning variant of Hamiltonian Monte Carlo (HMC) (Duane et al., 1987). This class of samplers works well on high dimensional and complex posterior distributions and allows many complex models to be ... Nov 10, 2020 · We’ve seen that there are different ways to write MCMC samplers by having more or less of the code written in JAX. On one hand, you can use JAX to write the log-posterior function and use Python/NumPy for the rest. On the other hand you can use JAX to write the entire sampler.

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