
Deep Involutive Generative Models for Neural MCMC
We introduce deep involutive generative models, a new architecture for d...
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Bayesian causal inference via probabilistic program synthesis
Causal inference can be formalized as Bayesian inference that combines a...
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Compositional Inference Metaprogramming with Convergence Guarantees
Inference metaprogramming enables effective probabilistic programming by...
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Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
Databases are widespread, yet extracting relevant data can be difficult....
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Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Nonparametric Bayes
Datasets with hundreds of variables and many missing values are commonpl...
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Probabilistic Data Analysis with Probabilistic Programming
Probabilistic techniques are central to data analysis, but different app...
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BayesDB: A probabilistic programming system for querying the probable implications of data
Is it possible to make statistical inference broadly accessible to nons...
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CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
There is a widespread need for statistical methods that can analyze high...
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A New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive p...
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Automatic Inference for Inverting Software Simulators via Probabilistic Programming
Models of complex systems are often formalized as sequential software si...
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Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Particle Markov chain Monte Carlo techniques rank among current stateof...
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SublinearTime Approximate MCMC Transitions for Probabilistic Programs
Probabilistic programming languages can simplify the development of mach...
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Venture: a higherorder probabilistic programming platform with programmable inference
We describe Venture, an interactive virtual machine for probabilistic pr...
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Variational Particle Approximations
Approximate inference in highdimensional, discrete probabilistic models...
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Building fast Bayesian computing machines out of intentionally stochastic, digital parts
The brain interprets ambiguous sensory information faster and more relia...
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Structured Priors for Structure Learning
Traditional approaches to Bayes net structure learning typically assume ...
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Church: a language for generative models
We introduce Church, a universal language for describing stochastic gene...
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Vikash Mansinghka
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Researcher in artificial intelligence and probabilistic computing, Leader of the MIT Probabilistic Computing Project at Massachusetts Institute of Technology