University of Regina

The game-theoretic framework, introduced by Vovk and myself in 2001 (www.probabilityandfinance.com), uses game theory instead of measure theory as a mathematical framework for probability. Classical theorems are proven by betting strategies that...

The main goal of this presentation is to describe an architecture for solving large hybrid Bayesian networks (BNs) with discrete, continuous and deterministic variables. In the presence of deterministic variables, we have to deal with non-existence...

A Bayesian network is a compact and intuitive graphical representation of the joint probability distribution. Belief update in a Bayesian network is the task of computing posterior probability distributions of unobserved variables given a set of...

Probabilistic and decision-theoretic graphical models, such as Bayesian networks, provide compact yet powerful formalisms for building intelligent systems, known as agents in the field of artificial intelligence, that must function in partially...

The main goal of this presentation is to describe an architecture for solving large hybrid Bayesian networks (BNs) with discrete, continuous and deterministic variables. In the presence of deterministic variables, we have to deal with non-existence...

The game-theoretic framework, introduced by Vovk and myself in 2001 (www.probabilityandfinance.com), uses game theory instead of measure theory as a mathematical framework for probability. Classical theorems are proven by betting strategies that...

Graphical models that represent knowledge and uncertainty such as Bayesian networks are popular approaches due to their strong probabilistic semantics and oftentimes intuitive decision modeling capabilities. In this talk, we will examine such...