POPL 2017
Sun 15 - Sat 21 January 2017
Wed 18 Jan 2017 14:45 - 15:10 at Amphitheater 44 - Probabilistic Programming Chair(s): Marco Gaboardi

Bayesian inference, of posterior knowledge from prior knowledge and observed evidence, is typically defined by Bayes’s rule, which says the posterior multiplied by the probability of an observation equals a joint probability. But the observation of a continuous quantity usually has probability zero, in which case Bayes’s rule says only that the unknown times zero is zero. To infer a posterior distribution from a zero-probability observation, the statistical notion of disintegration tells us to specify the observation as an expression rather than a predicate, but does not tell us how to compute the posterior. We present the first method of computing a disintegration from a probabilistic program and an expression of a quantity to be observed, even when the observation has probability zero. Because the method produces an exact posterior term and preserves a semantics in which monadic terms denote measures, it composes with other inference methods in a modular way—without sacrificing accuracy or performance.

Wed 18 Jan

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

14:20 - 16:00
Probabilistic ProgrammingPOPL at Amphitheater 44
Chair(s): Marco Gaboardi SUNY Buffalo, USA
14:20
25m
Talk
Beginner's Luck: A Language for Property-Based Generators
POPL
Leonidas Lampropoulos University of Pennsylvania, Diane Gallois-Wong Inria Paris, ENS Paris, Cătălin Hriţcu Inria Paris, John Hughes Chalmers University of Technology, Benjamin C. Pierce University of Pennsylvania, Li-yao Xia ENS Paris
Pre-print
14:45
25m
Talk
Exact Bayesian Inference by Symbolic Disintegration
POPL
Chung-chieh Shan Indiana University, USA, Norman Ramsey
Pre-print
15:10
25m
Talk
Stochastic Invariants for Probabilistic Termination
POPL
Krishnendu Chatterjee IST Austria, Petr Novotný IST Austria, Djordje Zikelic University of Cambridge
15:35
25m
Talk
Coupling proofs are probabilistic product programs
POPL
Gilles Barthe IMDEA, Benjamin Gregoire INRIA, Justin Hsu , Pierre-Yves Strub École Polytechnique