Carnegie Mellon University

Bayesian Inference

Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as evidence. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability".

Department Members with this Research Interest

7 bios displayed.

Philipp Burckhardt

Philipp Burckhardt

Postdoctoral Researcher

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Joseph Kadane

Joseph Kadane

Leonard J. Savage University Professor of Statistics and Social Sciences, Emeritus

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Cosma Shalizi

Cosma Shalizi

Associate Professor

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Lab Groups

There are currently no lab groups for this area of research.