UBC Theses and Dissertations
Accelerating Bayesian inference in probabilistic programming Munk, Andreas
This thesis focuses on the use of Bayesian inference and its practical application in real-world scenarios, such as in scientific stochastic simulators, via probabilistic programming. The execution of a probabilistic program (the reference program) is synonymous with probabilistic inference, and requires "only" the explicit denotation of random variables and their distributions. The inference procedure is carried out by a general-purpose inference backend (or engine) at runtime. The efficacy of many inference backends is a combination of (1) how well the particular engine can capture complex dependency structures between latent variables and (2) the speed at which the reference program runs---i.e. how fast its joint probability distribution can be calculated. Improvements to either attribute will result in more efficient inference. Furthermore, as these inference procedures---and Bayesian inference in general---require exact conditioning, it is not immediately obvious how to carry out inference when the conditional observations are associated with uncertainty. The main contributions of this thesis are the improvement of existing inference approaches in probabilistic programming by adding an attention mechanism to the inference back-end known as inference compilation and the extension of probabilistic programming to facilitate automated surrogate modeling. Additionally, this thesis make theoretical contributions to the problem of performing inference when observations are associated with uncertainty. In summary, this thesis aims to further advance the applicability of probabilistic programming in scientific simulators and Bayesian inference, with contributions focused on improving existing inference approaches, extending the functionality of probabilistic programming, and providing theoretical solutions to the challenges of uncertainty associated with observations. The results of this thesis have the potential to benefit researchers across many fields, ranging from physics to finance, by providing a more efficient and practical approach to simulator inversion and Bayesian inference.
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