Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs

by   Andreas Stuhlmüller, et al.

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.


page 1

page 2

page 3

page 4


Automated Variational Inference in Probabilistic Programming

We present a new algorithm for approximate inference in probabilistic pr...

Elements of Sequential Monte Carlo

A core problem in statistics and probabilistic machine learning is to co...

Predictive Coarse-Graining

We propose a data-driven, coarse-graining formulation in the context of ...

Compositional Inference Metaprogramming with Convergence Guarantees

Inference metaprogramming enables effective probabilistic programming by...

A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs

We describe a dynamic programming algorithm for computing the marginal d...

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm ...

Context-Specific Approximation in Probabilistic Inference

There is evidence that the numbers in probabilistic inference don't real...