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Supervised Learning of Bayesian Network Parameters Made Easy
 Level Perspective on Branch Architecture Performance, IEEE Micro28
, 2002
"... Bayesian network models are widely used for supervised prediction tasks such as classification. Usually the parameters of such models are determined using `unsupervised' methods such as maximization of the joint likelihood. In many cases, the reason is that it is not clear how to find the parameters ..."
Abstract

Cited by 4 (1 self)
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Bayesian network models are widely used for supervised prediction tasks such as classification. Usually the parameters of such models are determined using `unsupervised' methods such as maximization of the joint likelihood. In many cases, the reason is that it is not clear how to find the parameters maximizing the supervised (conditional) likelihood. We show how the supervised learning problem can be solved e#ciently for a large class of Bayesian network models, including the Naive Bayes (NB) and treeaugmented NB (TAN) classifiers. We do this by showing that under a certain general condition on the network structure, the supervised learning problem is exactly equivalent to logistic regression. Hitherto this was known only for Naive Bayes models. Since logistic regression models have a concave loglikelihood surface, the global maximum can be easily found by local optimization methods.
Author belongs to the Finnish Centre of Excellence in Algorithmic Data Analysis Research.
, 807
"... We study Bayesian discriminative inference given a model family p(c,x,θ) that is assumed to contain all our prior information but still known to be incorrect. This falls in between “standard ” Bayesian generative modeling and Bayesian regression, where the margin p(x,θ) is known to be uninformative ..."
Abstract
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We study Bayesian discriminative inference given a model family p(c,x,θ) that is assumed to contain all our prior information but still known to be incorrect. This falls in between “standard ” Bayesian generative modeling and Bayesian regression, where the margin p(x,θ) is known to be uninformative about p(cx, θ). We give an axiomatic proof that discriminative posterior is consistent for conditional inference; using the discriminative posterior is standard practice in classical Bayesian regression, but we show that it is theoretically justified for model families of joint densities as well. A practical benefit compared to Bayesian regression is that the standard methods of handling missing values in generative modeling can be extended into discriminative inference, which is useful if the amount of data is small. Compared to standard generative modeling, discriminative posterior results in better conditional inference if the model family is incorrect. If the model family contains also the true model, the discriminative posterior gives the same result as standard Bayesian generative modeling. Practical computation is done with Markov chain Monte Carlo. 1