Past exam of the mathematics course of the University of Cambridge / 2002 / iii / Paper 40 / 4 / a / ii

At temperature $T>0$, target a probability density function proportional to $\exp (f(\theta )/T)$ on a parameter region where it is normalizable, possibly with a specified proper base measure. A Metropolis–Hastings algorithm proposal $q(\theta ,{\theta }^{\prime })$ has acceptance probabilityFor a symmetric proposal, improving moves are accepted and a loss $\Delta$ is accepted with probability $e^{-\Delta /T}$. Run updates while gradually lowering $T$ toward zero. This simulated annealing scheme initially permits exploration and increasingly favors high objective values. Appropriate cooling and mixing conditions are needed for a general global convergence theorem; a finite arbitrary schedule does not guarantee finding a global maximum.

To maximize a positive likelihood function, take $f=\log L$, which has the same maximizers. The resulting likelihood-power annealing target is $L^{1/T}$, and available Gibbs sampler conditional distributions can replace Metropolis proposals.