Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs
This work addresses inference bottlenecks in probabilistic programming, offering a domain-specific improvement that is incremental in nature.
The paper tackles the problem of improving inference efficiency in probabilistic programs by proposing a coarse-to-fine transformation that generates data at increasing detail levels, showing preliminary evidence of enhanced efficiency in models like Ising and factorial hidden Markov models.
Many practical techniques for probabilistic inference require a sequence of distributions that interpolate between a tractable distribution and an intractable distribution of interest. Usually, the sequences used are simple, e.g., based on geometric averages between distributions. When models are expressed as probabilistic programs, the models themselves are highly structured objects that can be used to derive annealing sequences that are more sensitive to domain structure. We propose an algorithm for transforming probabilistic programs to coarse-to-fine programs which have the same marginal distribution as the original programs, but generate the data at increasing levels of detail, from coarse to fine. We apply this algorithm to an Ising model, its depth-from-disparity variation, and a factorial hidden Markov model. We show preliminary evidence that the use of coarse-to-fine models can make existing generic inference algorithms more efficient.