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Inferential Models
book

Inferential Models

by Ryan Martin, Chuanhai Liu
September 2015
Intermediate to advanced content levelIntermediate to advanced
276 pages
9h 7m
English
Chapman and Hall/CRC
Content preview from Inferential Models
84 PREDICTIVE RANDOM SETS
by the probability model X N(µ,1) and the constraint µ 0. The Gaussian model
for X allows any real-valued µ. For this unrestricted case, many inference methods
have proven to be simple and produce practically the same results for µ. Somewhat
surprisingly, when µ is known to belong to a restricted interval, the same problem
becomes challenging; see, for example, [81]. As discussed in [174], this problem
arises when measuring particle masses, which must be non-negative and are expected
to be relatively small, if nonzero.
In the Poisson example, the observed count, Y , is known to be comprised of signal
and background events, each coming from their own independent Poisson distribu-
tions. Suppose the background rate, b,
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Publisher Resources

ISBN: 9781439886519