
Bayesian Policy Search for Stochastic Domains
AI planning can be cast as inference in probabilistic models, and probab...
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Sufficiency, Separability and Temporal Probabilistic Models
Suppose we are given the conditional probability of one variable given s...
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Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Probabilistic programming languages (PPLs) are powerful modelling tools ...
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Paradoxes of Probabilistic Programming
Probabilistic programming languages allow programmers to write down cond...
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Lower and Upper Conditioning in Quantum Bayesian Theory
Updating a probability distribution in the light of new evidence is a ve...
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Stochastic Logic Programs: Sampling, Inference and Applications
Algorithms for exact and approximate inference in stochastic logic progr...
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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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Probabilistic Programs with Stochastic Conditioning
We propose to distinguish between deterministic conditioning, that is, conditioning on a sample from the joint data distribution, and stochastic conditioning, that is, conditioning on the distribution of the observable variable. Mostly, probabilistic programs follow the Bayesian approach by choosing a prior distribution of parameters and conditioning on observations. In a basic setting, individual observations are In a basic setting, individual observations are samples from the joint data distribution. However, observations may also be independent samples from marginal data distributions of each observable variable, summary statistics, or even data distributions themselves . These cases naturally appear in real life scenarios: samples from marginal distributions arise when different observations are collected by different parties, summary statistics (mean, variance, and quantiles) are often used to represent data collected over a large population, and data distributions may represent uncertainty during inference about future states of the world, that is, in planning. Probabilistic programming languages and frameworks which support conditioning on samples from the joint data distribution are not directly capable of expressing such models. We define the notion of stochastic conditioning and describe extensions of known general inference algorithms to probabilistic programs with stochastic conditioning. In case studies we provide probabilistic programs for several problems of statistical inference which are impossible or difficult to approach otherwise, perform inference on the programs, and analyse the results.
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