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.CNPq[3000183/98-4]FAPESP[04/09568-0]CAPE
The semigraphoid closure of every couple of CI-statements (CI=conditional independence) is a stochas...
We extend the theory of d-separation to cases in which data instances are not indepen-dent and ident...
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models f...
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...
One of the goals of artificial intelligence is to develop agents that learn and act in complex envir...
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The rules of d-separation provide a theoretical and algorithmic framework for deriving conditional i...
The implication problem is to test whether a given set of independencies logically implies another i...
In order to solve real-world tasks, intelligent machines need to be able to act in noisy worlds wher...
Independence and conditional independence are fundamental concepts for reasoning about groups of ran...
A logical concept of representation independence is developed for nonmonotonic logics, including pr...
The semigraphoid closure of every couple of CI-statements (CI=conditional independence) is a stochas...
We extend the theory of d-separation to cases in which data instances are not indepen-dent and ident...
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models f...
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...
One of the goals of artificial intelligence is to develop agents that learn and act in complex envir...
\u3cp\u3eThis papers investigates the manipulation of statements of strong independence in probabili...
The rules of d-separation provide a theoretical and algorithmic framework for deriving conditional i...
The implication problem is to test whether a given set of independencies logically implies another i...
In order to solve real-world tasks, intelligent machines need to be able to act in noisy worlds wher...
Independence and conditional independence are fundamental concepts for reasoning about groups of ran...
A logical concept of representation independence is developed for nonmonotonic logics, including pr...
The semigraphoid closure of every couple of CI-statements (CI=conditional independence) is a stochas...
We extend the theory of d-separation to cases in which data instances are not indepen-dent and ident...
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models f...