This paper on MCMC for machine learning may be of interest: www.cs.princeton.edu/courses/archive/spr06/cos598C/papers/AndrieuFreitasDouc etJordan2003.pdf
Regards, Michael Carman -----Original Message----- From: gretl-users-bounces(a)lists.wfu.edu [mailto:gretl-users-bounces(a)lists.wfu.edu] On Behalf Of Riccardo (Jack) Lucchetti Sent: Monday, 27 August 2012 9:40 PM To: Gretl list Subject: Re: [Gretl-users] MCMC for dummies? On Sat, 25 Aug 2012, Allin Cottrell wrote: > This may appear to be totally off-topic but it's not entirely so, > given that we've had a "feature request" at sourceforge for a Gibbs > sampler implementation. Anyway, does anyone have a recommendation for > a sort of "Markov Chain Monte Carlo for dummies" -- a useful book, > article or website? I'm no specialist on this, so I may be not entirely correct, but basically MCMC is a family of methods for drawing pseudo-random numbers from given (conditional) distributions. A good starting point is Chib and Greenberg(1995), "Understanding the Metropolis-Hastings Algorithm", The American Statistician, Vol. 49(4), pp. 327-335 or its predecessor, Casella and George(1992), "Explaining the Gibbs sampler", The American Statistician, Vol. 46(3), pp. 167-174. Once you've got a steady supply of pseudo-random draws, you may use them to simulate several useful object. Bayesians, for example, use them a lot to explore the posterior distribution of a parameter. In a frequentist context, you may use those draws to compute a simulated log-likelihood and then maximise it. See eg Gourieroux and Monfort(1996), Simulation-based econometric methods, Oxford UP This said, I think Lee is much more knowledgeable than me on this. Over to you, Prof. Adkins :) -------------------------------------------------- Riccardo (Jack) Lucchetti Dipartimento di Economia Università Politecnica delle Marche (formerly known as Università di Ancona) r.lucchetti(a)univpm.it http://www2.econ.univpm.it/servizi/hpp/lucchetti --------------------------------------------------
