A multitude of different probabilistic programming languages exists today, all extending a
traditional programming language with primitives to support modeling of complex, structured …

Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …

Recent developments in computational linguistics offer ways to approach the analysis of
musical structure by inducing probabilistic models (in the form of grammars) over a corpus of …

PRISM is a probabilistic extension of Prolog. It is a high level language for probabilistic
modeling capable of learning statistical parameters from observed data. After reviewing it …

Motivation: Probabilistic logic programming offers a powerful way to describe and evaluate
structured statistical models. To investigate the practicality of probabilistic logic programming …

Probabilistic Programming (PP)[Pfeiffer, 2016] has recently emerged as a useful tool for
building complex probabilistic models and for performing inference and learning on them …

We conduct a systematic comparison of several probabilistic models of symbolic music,
including zeroth and first order Markov models over pitches and intervals, a hidden Markov …

Conventional models of exemplar or rule-based concept learning tend to focus on the
acquisition of one concept at a time. They often underemphasize the fact that we learn many …

Abduction is one of the basic logical inferences (deduction, induction and abduction) and
derives the best explanations for our observation. Statistical abduction attempts to define a …

VT (Viterbi training), or hard expectation maximization (EM), is an efficient way of parameter
learning for probabilistic models with hidden variables. Given an observation y, it searches …