Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A tutorial on learning with Bayesian networks
Learning in graphical models
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
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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.