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Current Trends in Bayesian Methodology with Applications
book

Current Trends in Bayesian Methodology with Applications

by Satyanshu K. Upadhyay, Umesh Singh, Dipak K. Dey, Appaia Loganathan
May 2015
Intermediate to advanced content levelIntermediate to advanced
680 pages
22h 33m
English
Chapman and Hall/CRC
Content preview from Current Trends in Bayesian Methodology with Applications
292 Current Trends in Bayesian Methodology with Applications
14.2.2 Non-Adaptive SPS algorithm
We start from the SMC algorithm as detailed in [6]. The algorithm gener-
ates a nd modifies the particles θ
jn
, with superscripts used for further speci-
ficity at various points in the algo rithm. To make the notation compact, let
J = {1, . . . , J} and N = {1, . . . , N }. The algo rithm is an implementation of
Bayesian learning, providing simulations from θ | y
1:t
for t = 1, 2, . . . , T . It
processes observa tions, in order and in successive batches, e ach batch consti-
tuting a cycle of the algorithm.
The global structure of the algorithm is therefore iterative, proceeding
through the sample. But it operates on many particles in exactly the same
way at
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Publisher Resources

ISBN: 9781482235128