**D**uring his talk on unbiased MCMC in Dauphine today, Pierre Jacob provided a nice illustration of the convergence modes of MCMC algorithms. With the stationary target achieved after 100 Metropolis iterations, while the mean of the target taking much more iterations to be approximated by the empirical average. Plus a nice connection between coupling time and convergence. Convergence to the target.During Pierre’s talk, some simple questions came to mind, from developing an “impatient user version”, as in perfect sampling, in order to stop chains that run “forever”, to optimising parallelisation in order to avoid problems of asynchronicity. While the complexity of coupling increases with dimension and the coupling probability goes down, the average coupling time varies but an unexpected figure is that the expected cost per iteration is of 2 simulations, irrespective of the chosen kernels. Pierre also made a connection with optimal transport coupling and stressed that the maximal coupling was for the proposal and not for the target.

## Archive for maximal coupling

## convergences of MCMC and unbiasedness

Posted in pictures, Statistics, University life with tags asynchronous algorithms, Hastings-Metropolis sampler, impatient user, maximal coupling, MCMC convergence, optimal transport, parallelisation, Paris Dauphine, perfect sampling, unbiased MCMC on January 16, 2018 by xi'an## correlation for maximal coupling

Posted in Books, Kids, pictures, R, Statistics, University life with tags Boxing Day, cross validated, fields, maximal coupling, Pierre Jacob, R, Statisfaction on January 3, 2018 by xi'an**A**n interesting (if vaguely formulated) question on X validated: given two Gaussian variates that are maximally coupled, what is the correlation between these variates? The answer depends on the parameters of both Gaussian, with a correlation of one when both Gaussians are identical. Answering the question by simulation (as I could not figure out the analytical formula on Boxing Day…) led me back to Pierre Jacob’s entry on the topic on Statisfaction, where simulating the maximal coupling stems from the decompositions

p(x)=p(x)∧q(x)+{p(x)-p(x)∧q(x)} and q(x)=p(x)∧q(x)+{q(x)-p(x)∧q(x)}

and incidentally to the R function image.plot (from the R library fields) for including the side legend.