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UBC Theses and Dissertations

On amortized inference in large-scale simulators Naderiparizi, Saeid


Motivated by the problem of amortized inference in large-scale simulators, we introduce a probabilistic programming library that brings us closer to this goal. This library enables us to perform Bayesian inference on any simulator written in a wide variety of programming languages, with minimal modification to the simulator's source code. However, there are challenges in achieving this goal in its most general meaning. In particular, we address the obstacles caused by unbounded loops. Existing approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. An instance of this is importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure. We develop a new and efficient amortized importance sampling estimator. We prove finite variance of our estimator and empirically demonstrate our method's correctness and efficiency compared to existing alternatives on generative programs containing rejection sampling loops and discuss how to implement our method in a generic probabilistic programming framework.

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