
Bayesian causal inference via probabilistic program synthesis
Causal inference can be formalized as Bayesian inference that combines a...
10/30/2019 ∙ by Sam Witty, et al. ∙ 53 ∙ shareread it

Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
Databases are widespread, yet extracting relevant data can be difficult....
04/04/2017 ∙ by Feras Saad, et al. ∙ 0 ∙ shareread it

Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Nonparametric Bayes
Datasets with hundreds of variables and many missing values are commonpl...
11/05/2016 ∙ by Feras Saad, et al. ∙ 0 ∙ shareread it

BayesDB: A probabilistic programming system for querying the probable implications of data
Is it possible to make statistical inference broadly accessible to nons...
12/15/2015 ∙ by Vikash Mansinghka, et al. ∙ 0 ∙ shareread it

Probabilistic Data Analysis with Probabilistic Programming
Probabilistic techniques are central to data analysis, but different app...
08/18/2016 ∙ by Feras Saad, et al. ∙ 0 ∙ shareread it

Building fast Bayesian computing machines out of intentionally stochastic, digital parts
The brain interprets ambiguous sensory information faster and more relia...
02/20/2014 ∙ by Vikash Mansinghka, et al. ∙ 0 ∙ shareread it

CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
There is a widespread need for statistical methods that can analyze high...
12/03/2015 ∙ by Vikash Mansinghka, et al. ∙ 0 ∙ shareread it

A New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive p...
07/03/2015 ∙ by Frank Wood, et al. ∙ 0 ∙ shareread it

Automatic Inference for Inverting Software Simulators via Probabilistic Programming
Models of complex systems are often formalized as sequential software si...
05/31/2015 ∙ by Ardavan Saeedi, et al. ∙ 0 ∙ shareread it

Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Particle Markov chain Monte Carlo techniques rank among current stateof...
01/27/2015 ∙ by JanWillem van de Meent, et al. ∙ 0 ∙ shareread it

SublinearTime Approximate MCMC Transitions for Probabilistic Programs
Probabilistic programming languages can simplify the development of mach...
11/06/2014 ∙ by Yutian Chen, et al. ∙ 0 ∙ shareread it

Church: a language for generative models
We introduce Church, a universal language for describing stochastic gene...
06/13/2012 ∙ by Noah Goodman, et al. ∙ 0 ∙ shareread it

Venture: a higherorder probabilistic programming platform with programmable inference
We describe Venture, an interactive virtual machine for probabilistic pr...
04/01/2014 ∙ by Vikash Mansinghka, et al. ∙ 0 ∙ shareread it

Variational Particle Approximations
Approximate inference in highdimensional, discrete probabilistic models...
02/24/2014 ∙ by Ardavan Saeedi, et al. ∙ 0 ∙ shareread it

Structured Priors for Structure Learning
Traditional approaches to Bayes net structure learning typically assume ...
06/27/2012 ∙ by Vikash Mansinghka, et al. ∙ 0 ∙ shareread it

Compositional Inference Metaprogramming with Convergence Guarantees
Inference metaprogramming enables effective probabilistic programming by...
07/11/2019 ∙ by Shivam Handa, et al. ∙ 0 ∙ shareread it
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