An introduction to Markov Chain Monte Carlo (MCMC)

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I will introduce the concept of Markov Chain Monte Carlo and in particular the Metropolis-Hastings algorithm to generate samples from an arbitrary distribution. I will motivate the algorithm by discussing its relevance in Bayesian statistics to circumvent the evaluation of the marginal likelihood.
  1. Statistical Genomics
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Автор

Thank you so much for your video! Your explanation is very clear; I've learned much from it. Please make more videos about Bayesian and ODE MCMC. These topics are difficult and not many people can explain them as well as you.

thuylinhlinh
Автор

For likelihood, we have pareto distribution, and for theta prior follows Gamma distribution. We have 20 samples given . Now my posterior is complex, involving sum of logxi. What proposal density should i use and for sum logxi appeared with theta in posterior should i sum all data given what to do and how to proceed could you help

laxmividyaclasses
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