
#19
Dec612, 10:19 AM

P: 304

Ok, I understand this better now.
However I'm having trouble with the concept of "state" here. For a conventional (discrete time) Markov Chain, we have a row vector which tells us the probability of being in a state. So the size of the row vector is the number of states. I'm wondering what is the analogous vector for the Markov Chain we construct in the Metropolis Algorithm. Is it an infinite row vector, as there are infinitely many points in the parameter space? Then the transition matrix would also be infinite then. 



#20
Dec712, 10:41 PM

P: 523

If you have the time, some courses on measure theory and stochastic processes would certainly be worth your while, but the gist of it is that the same arguments for MetropolisHastings apply as long as the jump "density" is defined and nonzero between every pair of distinct lilypads. HTH 



#21
Dec1112, 03:36 PM

P: 304

Alright, thanks for the information.
Final query (I think) should a Markov Chain be aperiodic if its stationary distribution is to be explored by the Metropolis Algorithm? (and why?) 



#22
Dec2412, 02:27 PM

P: 3

I recommend the third edition of the book Understanding Probability (Cambridge University Press, 2012) by Henk Tijms. The author provides a very clear explanation of Markov chain Monte Carlo simulation. The explanation of the Metropolis algorithm allows one to understand how to construct an algorithm of their own. Nice illustrations are given.



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