
Sufficiency, Separability and Temporal Probabilistic Models
Suppose we are given the conditional probability of one variable given s...
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Bayesian Policy Search for Stochastic Domains
AI planning can be cast as inference in probabilistic models, and probab...
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Compositional Semantics for Probabilistic Programs with Exact Conditioning
We define a probabilistic programming language for Gaussian random varia...
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Densities of almostsurely terminating probabilistic programs are differentiable almost everywhere
We study the differential properties of higherorder statistical probabi...
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A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs
We describe a dynamic programming algorithm for computing the marginal d...
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An Evaluation of Structural Parameters for Probabilistic Reasoning: Results on Benchmark Circuits
Many algorithms for processing probabilistic networks are dependent on t...
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The geometry of learning
We establish a correspondence between classical conditioning processes a...
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Probabilistic Programs with Stochastic Conditioning
We tackle the problem of conditioning probabilistic programs on distributions of observable variables. Probabilistic programs are usually conditioned on samples from the joint data distribution, which we refer to as deterministic conditioning. However, in many reallife scenarios, the observations are given as marginal distributions, summary statistics, or samplers. Conventional probabilistic programming systems lack adequate means for modeling and inference in such scenarios. We propose a generalization of deterministic conditioning to stochastic conditioning, that is, conditioning on the marginal distribution of a variable taking a particular form. To this end, we first define the formal notion of stochastic conditioning and discuss its key properties. We then show how to perform inference in the presence of stochastic conditioning. We demonstrate potential usage of stochastic conditioning on several case studies which involve various kinds of stochastic conditioning and are difficult to solve otherwise. Although we present stochastic conditioning in the context of probabilistic programming, our formalization is general and applicable to other settings.
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