A streaming probabilistic program receives a stream of observations and produces distributions that are conditioned on these observations. Efficient inference is often possible in context using Rao-Blackwellized particle filters (RBPFs), which exactly solve problems when fall back sampling approximations necessary. While RBPFs can be implemented by hand to provide efficient inference, the goal ...

Human thought is remarkably flexible: we can think about infinitely many different situations despite uncertainty and novelty. Probabilistic models of cognition (Chater, Tenenbaum, & Yuille, 2006) have been successful at explaining a wide variety of learning and reasoning under uncertainty. They have borrowed tools from statistics and machine learning to explain phenomena from perception (Yuill...

We introduce the language LP that extends logic programs under the stable model semantics to allow weighted rules similar to the way Markov Logic considers weighted formulas. LP is a proper extension of the stable model semantics to enable probabilistic reasoning, providing a way to handle inconsistency in answer set programming. We also show that the recently established logical relationship b...

Recent findings suggest that humans deploy cognitive mechanism of physics simulation engines to simulate the objects. We propose a framework for bots probabilistic programming tools interacting with intuitive environments. The employs in way infer about moves performed by an agent setting governed Newtonian laws motion. However, methods programs can be slow such due their need generate many sam...

Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an algorithm solve them. Popular algorithms for PPLs, such as sequential Monte Carlo (SMC) Markov chain (MCMC), are built around checkpoints -- relevant events the during execution of a probabilistic program. Deciding location is, in current not done optimally. To this problem...

This paper presents ProbCompCert, a compiler for subset of the Stan probabilistic programming language (PPL), in which several key passes have been formally verified using Coq proof assistant. Because nature PPLs, bugs their compilers can be difficult to detect and fix, making verification an interesting possibility. However, proving correctness PPL compilation requires new techniques because c...

This paper presents PFLP, a library for probabilistic programming in the functional logic programming language Curry. It demonstrates how the concepts of a functional logic programming language support the implementation of a library for probabilistic programming. In fact, the paradigms of functional logic and probabilistic programming are closely connected. That is, we can apply techniques fro...

In this paper, we focus on the combination of probabilistic logic programming with the principle of maximum entropy. We start by deening probabilistic queries to probabilistic logic programs and their answer substitutions under maximum entropy. We then present an ef-cient linear programming characterization for the problem of deciding whether a probabilistic logic program is satissable. Finally...

In this paper, we focus on the combination of probabilistic logic programming with the principle of maximum entropy. We start by deening probabilistic queries to probabilistic logic programs and their answer substitutions under maximum entropy. We then present an eecient linear programming characterization for the problem of deciding whether a probabilistic logic program is satissable. Finally,...