490 citations · 1k across the 74 of their papers we have counts for
7 papers · 1 filter
Encapsulating models and approximate inference programs in probabilistic modules
Marco F. Cusumano-Towner, Vikash K. Mansinghka
This paper introduces the probabilistic module interface, which allows encapsulation of complex probabilistic models with latent variables alongside custom stochastic approximate i…
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
Marco F. Cusumano-Towner, Vikash K. Mansinghka
A key limitation of sampling algorithms for approximate inference is that it is difficult to quantify their approximation error. Widely used sampling schemes, such as sequential im…
Time Series Structure Discovery via Probabilistic Program Synthesis
Ulrich Schaechtle, Feras Saad, Alexey Radul +1
There is a widespread need for techniques that can discover structure from time series data. Recently introduced techniques such as Automatic Bayesian Covariance Discovery (ABCD) p…
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Feras Saad, Vikash Mansinghka
Datasets with hundreds of variables and many missing values are commonplace. In this setting, it is both statistically and computationally challenging to detect true predictive rel…
Probabilistic Data Analysis with Probabilistic Programming
Feras Saad, Vikash Mansinghka
Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative populat…
Quantifying the probable approximation error of probabilistic inference programs
Marco F Cusumano-Towner, Vikash K Mansinghka
This paper introduces a new technique for quantifying the approximation error of a broad class of probabilistic inference programs, including ones based on both variational and Mon…