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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
Bayesian Logistic Regression Using Sequential Posterior Simulation 297
vector θ. In the standard setup θ
= (θ
1
, . . . , θ
C
) and
P (Y
t
= c | x
t
, θ) =
exp (θ
c
x
t
)
P
C
i=1
exp (θ
i
x
t
)
(c = 1, . . . , C; t = 1, . . . , T ) . (14 .13)
There is typically a normalization θ
c
= 0 for some c {1, 2 , . . . , C}, and there
could be further restrictions on θ, but these details are not important to the
main points of this s e c tion.
We use the specification (14.13) of the multinomial logit model throughout.
The binomial logit model is the specia l case C = 2. Going forward, denote
the observed outcomes y
t
= (y
1
, . . . , y
T
) and the full set of c ovariates X =
[x
1
, .
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Publisher Resources

ISBN: 9781482235128