(c) 2017 Justin Bois. This work is licensed under a Creative Commons Attribution License CC-BY 4.0. All code contained herein is licensed under an MIT license.
This tutorial exercise was generated from an Jupyter notebook. You can download the notebook here.
a) Explain in words why being able to sample out of a probability distribution is useful.
b) Explain in words the basic idea behind Markov chain Monte Carlo.
Why is it important to "burn in" (a.k.a. "tune" or "warm up") walkers when performing a MCMC calculation?
Say we used MCMC to sample a posterior distribution that had 6 parameters, $g(a_1,a_2,a_3,a_4,a_5,a_6\mid D)$. From the MCMC samples, how can we get samples for the marginalized distribution $g(a_3\mid D)$?