anytime!

“An anytime algorithm is an algorithm that can be run continuously, generating progressively better solutions when afforded additional computation time. Traditional particle-based inference algorithms are not anytime in nature; all particles need to be propagated in lock-step to completion in order to compute expectations.”

Following a discussion with Lawrence Murray last week, I read Paige et al.  NIPS 2014 paper on their anytime sequential Monte Carlo algorithm. As explained above, an anytime algorithm is interruptible, meaning it can be stopped at any time without biasing the outcome of the algorithm. While MCMC algorithms can qualify as anytime (provided they are in stationary regime), it is not the case with sequential and particle Monte Carlo algorithms, which do not have an inbred growing mechanism preserving the target. In the case of Paige et al.’s proposal, the interruptible solution returns an unbiased estimator of the marginal likelihood at time n for any number of particles, even when this number is set or increased during the computation. The idea behind the solution is to create a particle cascade by going one particle at a time and creating children of this particle in proportion to the current average weight. An approach that can be run indefinitely. And since memory is not infinite, the authors explain how to cap the number of alive particles without putting the running distribution in jeopardy…

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