Computer Science > Programming Languages
[Submitted on 11 Jul 2019 (v1), last revised 15 Jul 2019 (this version, v2)]
Title:Compositional Inference Metaprogramming with Convergence Guarantees
View PDFAbstract:Inference metaprogramming enables effective probabilistic programming by supporting the decomposition of executions of probabilistic programs into subproblems and the deployment of hybrid probabilistic inference algorithms that apply different probabilistic inference algorithms to different subproblems. We introduce the concept of independent subproblem inference (as opposed to entangled subproblem inference in which the subproblem inference algorithm operates over the full program trace) and present a mathematical framework for studying convergence properties of hybrid inference algorithms that apply different Markov-Chain Monte Carlo algorithms to different parts of the inference problem. We then use this formalism to prove asymptotic convergence results for probablistic programs with inference metaprogramming. To the best of our knowledge this is the first asymptotic convergence result for hybrid probabilistic inference algorithms defined by (subproblem-based) inference metaprogramming.
Submission history
From: Shivam Handa [view email][v1] Thu, 11 Jul 2019 19:07:49 UTC (860 KB)
[v2] Mon, 15 Jul 2019 14:57:54 UTC (860 KB)
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