3 citations · 13 across the 8 of their papers we have counts for
6 papers · 1 filter
Multi-Language Probabilistic Programming
Sam Stites, John M. Li, Steven Holtzen
There are many different probabilistic programming languages that are specialized to specific kinds of probabilistic programs. From a usability and scalability perspective, this is…
A Nominal Approach to Probabilistic Separation Logic
John M. Li, Jon Aytac, Philip Johnson-Freyd +2
Currently, there is a gap between the tools used by probability theorists and those used in formal reasoning about probabilistic programs. On the one hand, a probability theorist d…
Bit Blasting Probabilistic Programs
Poorva Garg, Steven Holtzen, Guy Van den Broeck +1
Probabilistic programming languages (PPLs) are expressive means for creating and reasoning about probabilistic models. Unfortunately hybrid probabilistic programs, involving both c…
Lilac: A Modal Separation Logic for Conditional Probability
John M. Li, Amal Ahmed, Steven Holtzen
We present Lilac, a separation logic for reasoning about probabilistic programs where separating conjunction captures probabilistic independence. Inspired by an analogy with mutabl…
Scaling Exact Inference for Discrete Probabilistic Programs
Steven Holtzen, Guy Van den Broeck, Todd Millstein
Probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge of probabilistic inference…
Symbolic Exact Inference for Discrete Probabilistic Programs
Steven Holtzen, Todd Millstein, Guy Van den Broeck
The computational burden of probabilistic inference remains a hurdle for applying probabilistic programming languages to practical problems of interest. In this work, we provide a…