scholarly journals Converting Graphic Relationships into Conditional Probabilities in Bayesian Network

2017 ◽  
Author(s):  
Loc Nguyen
Keyword(s):  
Bayesian Network ◽  
Conditional Probabilities

PREDICTION OF WEATHER-RELATED FAILURES OF OVERHEAD DISTRIBUTION FEEDERS

2005 ◽  
Vol 20 (1) ◽  
pp. 117-125 ◽  
Author(s):  
Yujia Zhou ◽  
Anil Pahwa ◽  
Sanjoy Das
Keyword(s):  
Linear Regression ◽  
Bayesian Network ◽  
Regression Function ◽  
Conditional Probabilities ◽  
Weather Factors ◽  
Distribution Feeder

This article presents two methods for predicting weather-related overhead distribution feeder failures. The first model is based on linear regression, which uses a regression function to determine the correlation between the weather factors and overhead feeder failures. The second method is based on a one-layer Bayesian network, which uses conditional probabilities to model the correlation. Both methods are discussed and followed by tests to assess their performance. The results obtained using these methods are discussed and compared.

Bayesian Networks

2011 ◽  
pp. 89-93 ◽  
Author(s):  
Ahmad Bashir ◽  
Latifur Khan ◽  
Mamoun Awad
Keyword(s):  
Neural Networks ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Decision Trees ◽  
Graphical Model ◽  
Conditional Probabilities ◽  
Rule Bases

A Bayesian network is a graphical model that finds probabilistic relationships among variables of a system. The basic components of a Bayesian network include a set of nodes, each representing a unique variable in the system, their inter-relations, as indicated graphically by edges, and associated probability values. By using these probabilities, termed conditional probabilities, and their interrelations, we can reason and calculate unknown probabilities. Furthermore, Bayesian networks have distinct advantages compared to other methods, such as neural networks, decision trees, and rule bases, which we shall discuss in this paper.

scholarly journals Exploiting Causal Independence in Bayesian Network Inference

1996 ◽  
Vol 5 ◽  
pp. 301-328 ◽  
Author(s):  
N. L. Zhang ◽  
D. Poole
Keyword(s):  
Bayesian Network ◽  
Medical Diagnosis ◽  
Network Inference ◽  
Empirical Studies ◽  
Joint Probability ◽  
Simple Algorithm ◽  
Conditional Probabilities ◽  
Causal Independence ◽  
New Formulation ◽  
Bayesian Network Inference

A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as ``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a simple algorithm VE for Bayesian network inference that, given evidence and a query variable, uses the factorization to find the posterior distribution of the query. We show how this algorithm can be extended to exploit causal independence. Empirical studies, based on the CPCS networks for medical diagnosis, show that this method is more efficient than previous methods and allows for inference in larger networks than previous algorithms.

Denoising of Images using wavelet Bayesian Network

2017 ◽  
Author(s):  
Prof. Anil Bavaskar ◽  
Sangita Kulkarni
Keyword(s):  
Bayesian Network

Bayesian network ensemble learning with inadequate information

2014 ◽  
Author(s):  
Ruijie Du ◽  
Shuangcheng Wang ◽  
Cuiping Leng ◽  
Yunbin Fu
Keyword(s):  
Bayesian Network ◽  
Ensemble Learning ◽  
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