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Unformatted text preview: p x j N Prob f DLT j Dose x g then the likelihood function for r = [ g N ] given the data D k is L N j D k P k i 1 p x i j N y i f 1 p x i j N g 1 y i : Bayes theorem then implies that the joint posterior distribution of ( g , N ) given the data D k is C k g ; N j D k L g ; N j D k h g ; N m L u j D k h u d u where the integral is over Q . To facilitate exposition, it will hereafter be assumed that the prior distribution h is dened on some set G V containing the parameter space Q such that g 2 G and N 2 V with prior probability 1. Whenever necessary, this will entail extending h from Q to G V by dening h to be identically equal to zero on the dierence ( G V )\ Q . This convention will simplify ensuing formulations without a loss of Copyright n 2004 by Marcel Dekker, Inc. All Rights Reserved....
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This note was uploaded on 06/13/2011 for the course PHYSICS 101 taught by Professor Shu during the Spring '11 term at FIU.
- Spring '11