If I use Gibbs sampling with a Bayesian model, what do I have to check is memoryless?


Right now I am trying to better understand how Bayesian modeling works with just the basics. I found through reading tutorials that some very basic Bayesian models like Bayesian Hierarchical Modeling use something called the "Gibbs sampling algorithm", which is a Markov Chain Monte Carlo Method algorithm.

I know that, if I am going to do anything with Markov Chains, then I have to test a data or parameter violates the assumption of memoryless. However, I am uncertain what exactly do I need to test for memorylessness. Is it a parameter, the dataset, the response variable, a particular residual or error distribution.... what is it that has to be memoryless if you use Gibbs Sampling?

(P.P.S.) I did find a paper on that test's memoryless for Bayesian models but its really confusing. I am a little familiar with how to test memorylessness for basic data, but I am lost on more complex algorithms like Gibbs Sampling.


Posted 2020-01-31T00:18:49.907

Reputation: 41

I know this might be a hard question, but please answer if you know of anything relevant. – pierround – 2020-02-05T14:23:48.300

No answers