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It would be nice to be able to do two things with MH sampling:
1) When a chain finishes properly, save the current state (position, covariance). Then use this to initialize a future run.
2) Restart t…
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@eb8680 had an implementation for single site Metropolis Hastings in #61. Let us resolve the issues raised in the PR, and reinstate the algorithm. This is also needed to build other algorithms like An…
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For a metropolis hastings withing gibbs we need to evaluate the prior and posterior (reconstruction) probability. The prior is easy to evaluate as we know it should be gaussian by design. For the post…
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model: 2bees, old, semysinch
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normal:
, it would be useful to have a variant of `mh` that exposes the acceptance probability. We could make something like `mh_with_dia…
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**What is the main topic of this tutorial**: Explain what are MCMC, Metropolis-Hastings, Hamiltonian Monte Carlo, and how to use them in practice.
**One line description**
This tutorial would help…
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Hi,
Can I calculate evidence from Monte Python run outputs under the Metropolis-Hastings algorithm? I was running with the CMB+Pantheon_Plus data set. After the convergence, I got all the chain fil…
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# What Dakota currently supports
the latest release of dakota: https://dakota.sandia.gov/2024/05/15/dakota-6-20/ supports: "five MCMC algorithms from MUQ: metropolis_hastings, adaptive_metropolis, …
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dear developpers,
I am quite new in sampling posterior distributions(so therefore Bayesian approach) using a MCMC technique based on Metropolis-Hastings algorithm. I am using the mcmc library in R fo…