Coupling from the past

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Among Markov chain Monte Carlo (MCMC) algorithms, coupling from the past is a method for sampling from the stationary distribution of a Markov chain. Contrary to many MCMC algorithms, coupling from the past gives in principle a perfect sample from the stationary distribution. It was invented by James Propp and David Wilson in 1996.

The basic idea[edit]

Consider a finite state irreducible aperiodic Markov chain M with state space S and (unique) stationary distribution \pi (\pi is a probability vector). Suppose that we come up with a probability distribution \mu on the set of maps f:S\to S with the property that for every fixed s\in S, its image f(s) is distributed according to the transition probability of M from state s. An example of such a probability distribution is the one where f(s) is independent from f(s') whenever s\ne s', but it is often worthwhile to consider other distributions. Now let f_j for j\in\mathbb Z be independent samples from \mu.

Suppose that x is chosen randomly according to \pi and is independent from the sequence f_j. (We do not worry for now where this x is coming from.) Then f_{-1}(x) is also distributed according to \pi, because \pi is M-stationary and our assumption on the law of f. Define

F_j:= f_{-1}\circ f_{-2}\circ\cdots\circ f_{-j}.

Then it follows by induction that F_j(x) is also distributed according to \pi for every j\in\mathbb N. Now here is the main point. It may happen that for some n\in\mathbb N the image of the map F_n is a single element of S. In other words, F_n(x)=F_n(y) for each y\in S. Therefore, we do not need to have access to x in order to compute F_n(x). The algorithm then involves finding some n\in \mathbb N such that F_n(S) is a singleton, and outputing the element of that singleton. The design of a good distribution \mu for which the task of finding such an n and computing F_n is not too costly is not always obvious, but has been accomplished successfully in several important instances[citation needed].

The monotone case[edit]

There is a special class of Markov chains in which there are particularly good choices for \mu and a tool for determining if |F_n(S)|=1. (Here |\cdot| denotes cardinality.) Suppose that S is a partially ordered set with order \le, which has a unique minimal element s_0 and a unique maximal element s_1; that is, every s\in S satisfies s_0\le s\le s_1. Also, suppose that \mu may be chosen to be supported on the set of monotone maps f:S\to S. Then it is easy to see that |F_n(S)|=1 if and only if F_n(s_0)=F_n(s_1), since F_n is monotone. Thus, checking this becomes rather easy. The algorithm can proceed by choosing n:=n_0 for some constant n_0, sampling the maps f_{-1},\dots,f_{-n}, and outputing F_n(s_0) if F_n(s_0)=F_n(s_1). If F_n(s_0)\ne F_n(s_1) the algorithm proceeds by doubling n and repeating as necessary until an output is obtained. (But the algorithm does not resample the maps f_{-j} which were already sampled; it uses the previously sampled maps when needed.)

References[edit]

  • Propp, James Gary; Wilson, David Bruce (1996), Exact sampling with coupled Markov chains and applications to statistical mechanics, "Proceedings of the Seventh International Conference on Random Structures and Algorithms (Atlanta, GA, 1995)", Random Structures & Algorithms 9 (1): 223–252, ISSN 1042-9832, MR 1611693 
  • Propp, James; Wilson, David (1998), "Coupling from the past: a user's guide", Microsurveys in discrete probability (Princeton, NJ, 1997), DIMACS Ser. Discrete Math. Theoret. Comput. Sci. 41, Providence, R.I.: American Mathematical Society, pp. 181–192, MR 1630414