490 citations · 1k across the 74 of their papers we have counts for
5 papers · 1 filter
Bayesian causal inference via probabilistic program synthesis
Sam Witty, Alexander Lew, David Jensen +1
Causal inference can be formalized as Bayesian inference that combines a prior distribution over causal models and likelihoods that account for both observations and interventions.…
Compositional Inference Metaprogramming with Convergence Guarantees
Shivam Handa, Vikash Mansinghka, Martin Rinard
Inference metaprogramming enables effective probabilistic programming by supporting the decomposition of executions of probabilistic programs into subproblems and the deployment of…
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle +2
We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-s…
Real-time Approximate Bayesian Computation for Scene Understanding
Javier Felip, Nilesh Ahuja, David Gómez-Gutiérrez +2
Consider scene understanding problems such as predicting where a person is probably reaching, or inferring the pose of 3D objects from depth images, or inferring the probable stree…
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions
Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman +1
The objective of goodness-of-fit testing is to assess whether a dataset of observations is likely to have been drawn from a candidate probability distribution. This paper presents…