
Scaling Exact Inference for Discrete Probabilistic Programs
Probabilistic programming languages (PPLs) are an expressive means of re...
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ConstraintBased Inference in Probabilistic Logic Programs
Probabilistic Logic Programs (PLPs) generalize traditional logic program...
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Exact Symbolic Inference in Probabilistic Programs via SumProduct Representations
We present the SumProduct Probabilistic Language (SPPL), a new system t...
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Composing inference algorithms as program transformations
Probabilistic inference procedures are usually coded painstakingly from ...
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The Libra Toolkit for Probabilistic Models
The Libra Toolkit is a collection of algorithms for learning and inferen...
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Constrained Bayesian Networks: Theory, Optimization, and Applications
We develop the theory and practice of an approach to modelling and proba...
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Probabilistic Selection in AgentSpeak(L)
Agent programming is mostly a symbolic discipline and, as such, draws li...
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Symbolic Exact Inference for Discrete Probabilistic Programs
The computational burden of probabilistic inference remains a hurdle for applying probabilistic programming languages to practical problems of interest. In this work, we provide a semantic and algorithmic foundation for efficient exact inference on discretevalued finitedomain imperative probabilistic programs. We leverage and generalize efficient inference procedures for Bayesian networks, which exploit the structure of the network to decompose the inference task, thereby avoiding full path enumeration. To do this, we first compile probabilistic programs to a symbolic representation. Then we adapt techniques from the probabilistic logic programming and artificial intelligence communities in order to perform inference on the symbolic representation. We formalize our approach, prove it sound, and experimentally validate it against existing exact and approximate inference techniques. We show that our inference approach is competitive with inference procedures specialized for Bayesian networks, thereby expanding the class of probabilistic programs which can be practically analyzed.
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