A new probabilistic-logic formalism, called CHRiSM, is introduced. CHRiSM is based on a combination of CHR and PRISM. It can be used for high-level rapid prototyping of complex statistical models by means of chance rules. The underlying PRISM system can then be used for several probabilistic inference tasks, including parameter learning. We describe a source-to-source transformation from CHRiSM rules to PRISM, via CHR(PRISM). Finally we discuss the relation between CHRiSM and probabilistic logic programming, in particular, CP-logic.status: publishe
A multitude of different probabilistic programming languages exists today, all extending a tradition...
Classical Constraint Handling Rules (CHR) provide a powerful tool for specifying and implementing co...
Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the...
PRISM is an extension of Prolog with probabilistic predicates and built-in support for expectation-m...
Abstract. Riveret et al. proposed a framework for probabilistic legal reasoning. Their goal is to de...
This papers develops a logical language for representing probabilistic causal laws. Our interest ...
Termination analysis has received considerable attention in Logic Programming for several decades. I...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
The tutorial will provide a motivation for, an overview of and an introduction to the fields of stat...
This papers develops a logical language for representing probabilistic causal laws. Our interest in ...
Probabilistic logic learning (PLL), sometimes also called statistical relational learning, addresses...
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertai...
A multitude of different probabilistic programming languages exists today, all extending a tradition...
A multitude of different probabilistic programming languages exists today, all extending a tradition...
Classical Constraint Handling Rules (CHR) provide a powerful tool for specifying and implementing co...
Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the...
PRISM is an extension of Prolog with probabilistic predicates and built-in support for expectation-m...
Abstract. Riveret et al. proposed a framework for probabilistic legal reasoning. Their goal is to de...
This papers develops a logical language for representing probabilistic causal laws. Our interest ...
Termination analysis has received considerable attention in Logic Programming for several decades. I...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
Probabilistic programming is an emerging subfield of AI that extends traditional programming languag...
The tutorial will provide a motivation for, an overview of and an introduction to the fields of stat...
This papers develops a logical language for representing probabilistic causal laws. Our interest in ...
Probabilistic logic learning (PLL), sometimes also called statistical relational learning, addresses...
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertai...
A multitude of different probabilistic programming languages exists today, all extending a tradition...
A multitude of different probabilistic programming languages exists today, all extending a tradition...
Classical Constraint Handling Rules (CHR) provide a powerful tool for specifying and implementing co...
Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the...