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Augur: a Modeling Language for Data-Parallel Probabilistic Inference
It is time-consuming and error-prone to implement inference procedures f...
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Formal Verification of Probabilistic SystemC Models with Statistical Model Checking
Transaction-level modeling with SystemC has been very successful in desc...
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Static Analysis for Probabilistic Programs
Probabilistic programming is a powerful abstraction for statistical mach...
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Datalog-based Scalable Semantic Diffing of Concurrent Programs
When an evolving program is modified to address issues related to thread...
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Efficient compilation of array probabilistic programs
Probabilistic programming languages are valuable because they allow us t...
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DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
We present the preliminary high-level design and features of DynamicPPL....
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C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Lightweight, source-to-source transformation approaches to implementing ...
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Transforming Probabilistic Programs for Model Checking
Probabilistic programming is perfectly suited to reliable and transparent data science, as it allows the user to specify their models in a high-level language without worrying about the complexities of how to fit the models. Static analysis of probabilistic programs presents even further opportunities for enabling a high-level style of programming, by automating time-consuming and error-prone tasks. We apply static analysis to probabilistic programs to automate large parts of two crucial model checking methods: Prior Predictive Checks and Simulation-Based Calibration. Our method transforms a probabilistic program specifying a density function into an efficient forward-sampling form. To achieve this transformation, we extract a factor graph from a probabilistic program using static analysis, generate a set of proposal directed acyclic graphs using a SAT solver, select a graph which will produce provably correct sampling code, then generate one or more sampling programs. We allow minimal user interaction to broaden the scope of application beyond what is possible with static analysis alone. We present an implementation targeting the popular Stan probabilistic programming language, automating large parts of a robust Bayesian workflow for a wide community of probabilistic programming users.
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