adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals

In the plane to Birmingham, I was reading this recent arXived paper by Minh-Ngoc Tran, Michael K. Pitt, and Robert Kohn. The adaptive structure of their ACMH algorithm is based upon two parallel Markov chains, the former (called the trial chain) feeding the proposal densities of the later (called the main chain), bypassing the more traditional diminishing adaptation conditions. (Even though convergence actually follows from a minorisation condition.) These proposals are mixtures of t distributions fitted by variational Bayes approximations. Furthermore, the proposals are (a) reversible and (b) mixing local [dependent] and global [independent] components. One nice aspect of the reversibility is that the proposals do not have to be evaluated at each step.

The convergence results in the paper indeed assume a uniform minorisation condition on all proposal densities: although this sounded restrictive at first (but allows for straightforward proofs), I realised this could be implemented by adding a specific component to the mixture as in Corollary 3. (I checked the proof to realise that the minorisation on the proposal extends to the minorisation on the Metropolis-Hastings transition kernel.) A reversible kernel is defined as satisfying the detailed balance condition, which means that a single Gibbs step is reversible even though the Gibbs sampler as a whole is not. If a reversible Markov kernel with stationary distribution ζ is used, the acceptance probability in the Metropolis-Hastings transition is

α(x,z) = min{1,π(z)ζ(x)/π(x)ζ(z)}

(a result I thought was already known). The sweet deal is that the transition kernel involves Dirac masses, but the acceptance probability bypasses the difficulty. The way mixtures of t distributions can be reversible follows from Pitt & Walker (2006) construction, with  ζ  a specific mixture of t distributions. This target is estimated by variational Bayes. The paper further bypasses my classical objection to the use of normal, t or mixtures thereof, distributions:  this modelling assumes a sort of common Euclidean space for all components, which is (a) highly restrictive and (b) very inefficient in terms of acceptance rate. Instead, Tran & al. resort to Metropolis-within-Gibbs by constructing a partition of the components into subgroups.

One Response to “adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals”

  1. It sounds cool, even though I don’t fully understand it (e.g. the reference to Dirac Mass, among others)

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