113 citations · 491 across the 32 of their papers we have counts for
4 papers · 1 filter
On the Pitfalls of Nested Monte Carlo
Tom Rainforth, Robert Cornish, Hongseok Yang +1
There is an increasing interest in estimating expectations outside of the classical inference framework, such as for models expressed as probabilistic programs. Many of these conte…
Design and Implementation of Probabilistic Programming Language Anglican
David Tolpin, Jan Willem van de Meent, Hongseok Yang +1
Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We introduce the programming language Anglican, outline our design cho…
Spreadsheet Probabilistic Programming
Mike Wu, Yura Perov, Frank Wood +1
Spreadsheet workbook contents are simple programs. Because of this, probabilistic programming techniques can be used to perform Bayesian inversion of spreadsheet computations. What…
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
Sam Staton, Hongseok Yang, Chris Heunen +2
We study the semantic foundation of expressive probabilistic programming languages, that support higher-order functions, continuous distributions, and soft constraints (such as Ang…