: This paper addresses the inference of probabilistic classification models using weakly supervised learning. The main contribution of this work is the development of learning meth...
We investigate the construction of linear operators representing the semantics of probabilistic programming languages expressed via probabilistic transition systems. Finite transi...
Abstract. Logical Bayesian Networks (LBNs) have recently been introduced as another language for knowledge based model construction of Bayesian networks, besides existing languages...
Daan Fierens, Hendrik Blockeel, Maurice Bruynooghe...
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 ...
Abstract. We present a formal framework for syntax directed probabilistic program analysis. Our focus is on probabilistic pointer analysis. We show how to obtain probabilistic poin...
Alessandra Di Pierro, Chris Hankin, Herbert Wiklic...