Paper:	Computational Technology for Bayesian Inference
Volume:	172, Astronomical Data Analysis Software and Systems VIII
Page:	297
Authors:	Loredo, Thomas J.
Abstract:	One of the most important practical differences between the Bayesian approach to statistical inference and the more traditional frequentist approach is the nature of the sums and integrals required to implement each approach. In both approaches, the sampling distribution for the data (i.e., the likelihood function) plays a key role; but frequentist calculations integrate the sampling distribution over the sample space, whereas Bayesian calculations integrate it over hypothesis (parameter) space. The numerous advantages to working in parameter space come at a cost: the required integrals are difficult to calculate. I survey recent developments in computational technology for performing such integrals.

Astronomical Society of the Pacific
Conference Series

© Copyright 1988 - 2024 - Astronomical Society of the Pacific
No part of the material protected by this copyright may be reproduced or used in any form other than for personal use without written permission from the ASP.

Page generated in 3.119 seconds