A survey on Bayesian network structure learning from data
A necessary step in the development of artificial intelligence is to enable a machine to
represent how the world works, building an internal structure from data. This structure should …
represent how the world works, building an internal structure from data. This structure should …
[图书][B] Plan, activity, and intent recognition: Theory and practice
Plan recognition, activity recognition, and intent recognition together combine and unify
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …
[PDF][PDF] A scoring function for learning Bayesian networks based on mutual information and conditional independence tests.
LM De Campos, N Friedman - Journal of Machine Learning Research, 2006 - jmlr.org
We propose a new scoring function for learning Bayesian networks from data using score+
search algorithms. This is based on the concept of mutual information and exploits some …
search algorithms. This is based on the concept of mutual information and exploits some …
Learning Analytics to identify dropout factors of Computer Science studies through Bayesian networks
ABSTRACT Student dropout in Engineering Education is an important problem which has
been studied from different perspectives, as well as using different techniques. This …
been studied from different perspectives, as well as using different techniques. This …
[PDF][PDF] Canonical probabilistic models for knowledge engineering
FJ Dıez, MJ Druzdzel - … , Madrid, Spain, Technical Report CISIAD-06, 2006 - 62.204.199.55
The hardest task in knowledge engineering for probabilistic graphical models, such as
Bayesian networks and influence diagrams, is obtaining their numerical parameters. Models …
Bayesian networks and influence diagrams, is obtaining their numerical parameters. Models …
Bayesian network learning algorithms using structural restrictions
LM de Campos, JG Castellano - International Journal of Approximate …, 2007 - Elsevier
The use of several types of structural restrictions within algorithms for learning Bayesian
networks is considered. These restrictions may codify expert knowledge in a given domain …
networks is considered. These restrictions may codify expert knowledge in a given domain …
Predicting dementia development in Parkinson's disease using Bayesian network classifiers
DA Morales, Y Vives-Gilabert, B Gómez-Ansón… - Psychiatry Research …, 2013 - Elsevier
Parkinson's disease (PD) has broadly been associated with mild cognitive impairment
(PDMCI) and dementia (PDD). Researchers have studied surrogate, neuroanatomic …
(PDMCI) and dementia (PDD). Researchers have studied surrogate, neuroanatomic …
Inference in hybrid Bayesian networks
Since the 1980s, Bayesian networks (BNs) have become increasingly popular for building
statistical models of complex systems. This is particularly true for boolean systems, where …
statistical models of complex systems. This is particularly true for boolean systems, where …
Probabilistic graphical models in artificial intelligence
P Larrañaga, S Moral - Applied soft computing, 2011 - Elsevier
In this paper, we review the role of probabilistic graphical models in artificial intelligence. We
start by giving an account of the early years when there was important controversy about the …
start by giving an account of the early years when there was important controversy about the …