
Exact Symbolic Inference in Probabilistic Programs via SumProduct Representations
We present the SumProduct Probabilistic Language (SPPL), a new system t...
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PClean: Bayesian Data Cleaning at Scale with DomainSpecific Probabilistic Programming
Data cleaning can be naturally framed as probabilistic inference in a ge...
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Automating Involutive MCMC using Probabilistic and Differentiable Programming
Involutive MCMC is a unifying mathematical construction for MCMC kernels...
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Online Bayesian Goal Inference for BoundedlyRational Planning Agents
People routinely infer the goals of others by observing their actions ov...
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The Fast Loaded Dice Roller: A NearOptimal Exact Sampler for Discrete Probability Distributions
This paper introduces a new algorithm for the fundamental problem of gen...
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Optimal Approximate Sampling from Discrete Probability Distributions
This paper addresses a fundamental problem in random variate generation:...
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Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
We present new techniques for automatically constructing probabilistic p...
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A Family of Exact GoodnessofFit Tests for HighDimensional Discrete Distributions
The objective of goodnessoffit testing is to assess whether a dataset ...
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Using probabilistic programs as proposals
Monte Carlo inference has asymptotic guarantees, but can be slow when us...
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A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series
This article proposes a Bayesian nonparametric method for forecasting, i...
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AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Approximate probabilistic inference algorithms are central to many field...
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Probabilistic programs for inferring the goals of autonomous agents
Intelligent systems sometimes need to infer the probable goals of people...
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Encapsulating models and approximate inference programs in probabilistic modules
This paper introduces the probabilistic module interface, which allows e...
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Measuring the nonasymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
A key limitation of sampling algorithms for approximate inference is tha...
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Quantifying the probable approximation error of probabilistic inference programs
This paper introduces a new technique for quantifying the approximation ...
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Probabilistic Programming with Gaussian Process Memoization
Gaussian Processes (GPs) are widely used tools in statistics, machine le...
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JUMPMeans: SmallVariance Asymptotics for Markov Jump Processes
Markov jump processes (MJPs) are used to model a wide range of phenomena...
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Inverse Graphics with Probabilistic CAD Models
Recently, multiple formulations of vision problems as probabilistic inve...
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Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
The idea of computer vision as the Bayesian inverse problem to computer ...
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ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures
The Dirichlet process (DP) is a fundamental mathematical tool for Bayesi...
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Vikash K. Mansinghka
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