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Sat 23 Jan 2016 17:00 - 17:20 at Room St Petersburg II - Session 5 Chair(s): Chung-chieh Shan

Our attempt to give semantics to a core, functional probabilistic programming language uses computable distributions. The semantics uses largely standard techniques from denotational semantics. One nice property of computable distributions is that they are completely characterized by a sampling algorithm. Hence, it is possible to faithfully implement (in a Turing-complete language) a semantics based on computable distributions as a sampling library. We are interested in the pros and cons of using computable distributions as opposed to measure theory in giving semantics to probabilistic programming languages.

Sat 23 Jan

pps-2016
16:30 - 18:00: PPS 2016 - Session 5 at Room St Petersburg II
Chair(s): Chung-chieh ShanIndiana University
pps-201616:30 - 16:50
Talk
Nathanael L. AckermanHarvard University, Cameron FreerGamalon, Daniel RoyUniversity of Toronto
Pre-print
pps-201616:50 - 17:00
Meeting
pps-201617:00 - 17:20
Talk
Daniel HuangHarvard University, Greg MorrisettCornell University
Pre-print
pps-201617:20 - 17:30
Meeting
pps-201617:30 - 17:50
Talk
Friedrich GretzRWTH Aachen University, Nils JansenRWTH Aachen University, Benjamin Lucien KaminskiRWTH Aachen University, Joost-Pieter KatoenRWTH Aachen University, Federico OlmedoRWTH Aachen University
Pre-print
pps-201617:50 - 18:00
Meeting