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Principles of Statistical Inference: Likelihood and the Bayesian Paradigm

Published online by Cambridge University Press:  21 July 2017

Steve C. Wang
Steve C. Wang*
Affiliation:
Department of Geological and Environmental Sciences, Stanford University, 450 Serra Mall, Building 320, Stanford, CA 94306 Department of Mathematics and Statistics, Swarthmore College, 500 College Ave, Swarthmore, PA 19081
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Abstract

We review two foundations of statistical inference, the theory of likelihood and the Bayesian paradigm. We begin by applying principles of likelihood to generate point estimators (maximum likelihood estimators) and hypothesis tests (likelihood ratio tests). We then describe the Bayesian approach, focusing on two controversial aspects: the use of prior information and subjective probability. We illustrate these analyses using simple examples.

Type
General Toolkit
Information
The Paleontological Society Papers , Volume 16: Quantitative Methods in Paleobiology , October 2010 , pp. 1 - 18
Copyright
Copyright © 2010 by the Paleontological Society 

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