We identify two problems with Wingate et al. lightweight implementation technique for probabilistic programs.  Simple examples demonstrate that common, what should be semantic-preserving program transformations drastically alter the program behavior. We briefly describe an alternative technique that does respect the program refactoring. … Read the rest

We extend a stream-processing programming language, whose denotational semantics is given using the domain of streams, so that it can support probabilistic action.  We give a denotational semantics to the extended language (which we call SProcP).  We take the domain of probability distributions over streams as the denotational domain; it constitutes a cpo according to Saheb-Djaromi [3].… Read the rest

This is an abstract of a poster by Larry Moss,  and Chung-chieh Shan, and Alexandra Silva. The one sentence summary of our proposal is to adapt ideas from coalgebraic semantics to the probabilistic setting, thereby obtaining intuitive semantics for probabilistic processes of various sorts.… Read the rest

We propose to write denotational semantics for a probabilistic programming language in terms of reproducing kernel Hilbert spaces for characteristic kernels. This opens up possibilities for providing convergence guarantees for approximate expansions, as well as practical advantages of using kernel methods for machine learning.… Read the rest

The goal of this paper is to advertise the application of fixed points and $latex \omega$-complete partial orders ($latex \omega$-cpos) in the formalization and analysis of probabilistic programming languages. The presented work is formalized in the Isabelle theorem prover.

By applying $latex \omega$-cpos to the analysis of MDPs we get a nice theory of fixed points.… Read the rest