Learning and Inferences of the Bayesian Network with Maximum Likelihood Parameters

Authors:
Jiadong Zhang;Kun Yue;Weiyi Liu
Affiliations:
Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, P.R. China 650091;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, P.R. China 650091;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, P.R. China 650091
Venue:
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Year:
2008

Citing 7
Cited 1

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian networks

Communications of the ACM
Machine Learning

Machine Learning
Learning Bayesian networks from data: an information-theory based approach

Artificial Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Augmenting learning function to Bayesian network inferences with maximum likelihood parameters

Expert Systems with Applications: An International Journal
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach

Characterizing human grasping movements by means of an acceleration sensor

ACC'11/MMACTEE'11 Proceedings of the 13th IASME/WSEAS international conference on Mathematical Methods and Computational Techniques in Electrical Engineering conference on Applied Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In real applications established on Bayesian networks (BNs), it is necessary to make inference for arbitrary evidence even it is not contained in existing conditional probability tables (CPTs). Aiming at this problem, in this paper, we discuss the learning and inferences of the BN with maximum likelihood parameters that replace the CPTs. We focus on the learning of the maximum likelihood parameters and give the corresponding methods for 2 kinds of BN inferences: forward inferences and backward inferences. Furthermore, we give the approximate inference method of BNs with maximum likelihood hypotheses. Preliminary experiments show the feasibility of our proposed methods.