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Bayesian Machine Learning

Bayesian Machine Learning

Eitel J.M. Lauria
Copyright: © 2005 |Pages: 7
ISBN13: 9781591405535|ISBN10: 159140553X|EISBN13: 9781591407942
DOI: 10.4018/978-1-59140-553-5.ch043
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MLA

Lauria, Eitel J.M. "Bayesian Machine Learning." Encyclopedia of Information Science and Technology, First Edition, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2005, pp. 229-235. https://doi.org/10.4018/978-1-59140-553-5.ch043

APA

Lauria, E. J. (2005). Bayesian Machine Learning. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, First Edition (pp. 229-235). IGI Global. https://doi.org/10.4018/978-1-59140-553-5.ch043

Chicago

Lauria, Eitel J.M. "Bayesian Machine Learning." In Encyclopedia of Information Science and Technology, First Edition, edited by Mehdi Khosrow-Pour, D.B.A., 229-235. Hershey, PA: IGI Global, 2005. https://doi.org/10.4018/978-1-59140-553-5.ch043

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Abstract

Bayesian methods provide a probabilistic approach to machine learning. The Bayesian framework allows us to make inferences from data using probability models for values we observe and about which we want to draw some hypotheses. Bayes theorem provides the means of calculating the probability of a hypothesis (posterior probability) based on its prior probability, the probability of the observations and the likelihood that the observational data fit the hypothesis.

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