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Using probabilistic programs as proposals

We show how custom Monte Carlo proposal distributions can be expressed as samplers written in probabilistic programming languages. We call these probabilistic programs proposal programs. Proposal programs allow the inference practitioner to naturally express their knowledge about a target distribution … Continue reading

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SlicStan: Improving Probabilistic Programming using Information Flow Analysis

Probabilistic programming languages provide a concise and abstract way to specify probabilistic models, while hiding away the underlying inference algorithm. However, those languages are often either not efficient enough to use in practice, or restrict the range of supported models … Continue reading

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Interactive Writing and Debugging of Bayesian Probabilistic Programs

We present an implementation of BLOG that facilitates fluid exploration of probabilistic programs. ¬†We introduce a new keyword inspect that allows the evaluation of an arbitrary expression in a given trace from the full posterior. Using this additional keyword, we … Continue reading

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Reasoning about Divergences via Span-liftings

We give a semantic framework for formal verifications of continuous probabilistic programming language for the recent relaxations of differential privacy: Renyi differential privacy and zero-Concentrated differential privacy. These relaxations can be good definitions of data privacy of machine learning mechanisms … Continue reading

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Probabilistic Program Inference With Abstractions

Abstraction is a fundamental tool in the analysis and verification of programs. Typically, a program abstraction selectively models particular aspects of the original program while utilizing non-determinism to conservatively account for other behaviors. However, non-deterministic abstractions do not directly apply … Continue reading

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Probabilistic Models for Assured Position, Navigation, and Timing

Position, Navigation, and Timing (PNT) platforms provide fundamental support for critical infrastructure, ranging from air traffic control, emergency services, telecom, financial markets, personal navigation, power grids, space applications, etc. However, the problem of defining PNT assurance metrics remains open. We … Continue reading

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Combining Static and Dynamic Optimizations Using Closed-Form Solutions

It is sometimes possible to optimize probabilistic programs, either statically or dynamically. We introduce two examples demonstrating the need for both approaches. Furthermore, we identify a set of challenges related to the two approaches, and more importantly, how to combine … Continue reading

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