We examine the representation of judgements of stochastic independence in probabilistic logics. We focus on a relational logic where (i) judgements of stochastic independence are encoded by directed acyclic graphs, and (ii) probabilistic assessments are flexible in the sense that they are not required to specify a single probability measure. We discuss issues of knowledge representation and inference that arise from our particular combination of graphs, stochastic independence, logical formulas and probabilistic assessments. (C) 2007 Elsevier B.V. All rights reserved
The semigraphoid closure of every couple of CI-statements (CI=conditional independence) is a stochas...
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models f...
Probabilistic logics have attracted a great deal of attention during the past few years. Where logic...
We examine the representation of judgements of stochastic independence in probabilistic logics. We f...
AbstractWe examine the representation of judgements of stochastic independence in probabilistic logi...
AbstractThis paper investigates probabilistic logics endowed with independence relations. We review ...
This paper investigates probabilistic logics endowed with independence relations. We review proposit...
This paper investigates probabilistic logics endowed with independence relations. We review proposit...
\u3cp\u3eThis papers investigates the manipulation of statements of strong independence in probabili...
One of the goals of artificial intelligence is to develop agents that learn and act in complex envir...
The rules of d-separation provide a theoretical and algorithmic framework for deriving conditional i...
A logical concept of representation independence is developed for nonmonotonic logics, including pr...
The implication problem is to test whether a given set of independencies logically implies another i...
Independence and conditional independence are fundamental concepts for reasoning about groups of ran...
In order to solve real-world tasks, intelligent machines need to be able to act in noisy worlds wher...
The semigraphoid closure of every couple of CI-statements (CI=conditional independence) is a stochas...
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models f...
Probabilistic logics have attracted a great deal of attention during the past few years. Where logic...
We examine the representation of judgements of stochastic independence in probabilistic logics. We f...
AbstractWe examine the representation of judgements of stochastic independence in probabilistic logi...
AbstractThis paper investigates probabilistic logics endowed with independence relations. We review ...
This paper investigates probabilistic logics endowed with independence relations. We review proposit...
This paper investigates probabilistic logics endowed with independence relations. We review proposit...
\u3cp\u3eThis papers investigates the manipulation of statements of strong independence in probabili...
One of the goals of artificial intelligence is to develop agents that learn and act in complex envir...
The rules of d-separation provide a theoretical and algorithmic framework for deriving conditional i...
A logical concept of representation independence is developed for nonmonotonic logics, including pr...
The implication problem is to test whether a given set of independencies logically implies another i...
Independence and conditional independence are fundamental concepts for reasoning about groups of ran...
In order to solve real-world tasks, intelligent machines need to be able to act in noisy worlds wher...
The semigraphoid closure of every couple of CI-statements (CI=conditional independence) is a stochas...
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models f...
Probabilistic logics have attracted a great deal of attention during the past few years. Where logic...