(a) A random sample z = bution specified apart from an unknown real parameter 0. Based on a given prior distribution, the posterior density of 0 is p(0 a). Show that the Bayes estimate of 0 under the loss function (*1, 12,.. .,n) is available from a distri- L(t, 0) = (t-0)2 where t is any estimate of 0, is given by {E(0-1 | )}-1. 1 Scanned with Camicanner (b) It is believed that the number of arrivals in a queue will follow a Poisson distribution with mean 0 per hour. Obtain the likelihood function for 6 given that the number of arrivals in the last n hours were 1, 22, .., n (c) Deduce the posterior distribution cf 0 when the prior distribution is Gamma(a, B) and write down its probability density function. (d) Show that the Bayes estimate of 0 under the loss function specified in part (a) is given by a +s - 1 B+n where s E i. (c) Find the predictive distribution of y the number of arrivals in the next hour.

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(a) A random sample z = (x1,I2,. .. ,In) is available from a distri-
bution specified apart from an unknown real parameter 0. Based
on a given prior distribution, the posterior density of 0 is p(0 | 2).
Show that the Bayes estimate of 0 under the loss function
L(t, 0)
(t- 0)?
where t is any estimate of 0, is given by {E(0-1 | 2)}-1.
Scanned with CamScanner
(b) It is believed that the number of arrivals in a queue will follow a
Poisson distribution with mean 0 rer hour. Obtain the likelihood
function for 0 given that the number of arrivals in the last n hours
were 21, 12,., Dn.
(c) Deduce the posterior distribution of 0 when the prior distribution
is Gamma(a, B) and write down its probability density function.
(d) Show that the Bayes estimate of 0 under the loss function specified
in part (a) is given by
a + s - 1
B+n
where s =
(e) Find the predictive distribution of y the number of arrivals in the
next hour.
Transcribed Image Text:(a) A random sample z = (x1,I2,. .. ,In) is available from a distri- bution specified apart from an unknown real parameter 0. Based on a given prior distribution, the posterior density of 0 is p(0 | 2). Show that the Bayes estimate of 0 under the loss function L(t, 0) (t- 0)? where t is any estimate of 0, is given by {E(0-1 | 2)}-1. Scanned with CamScanner (b) It is believed that the number of arrivals in a queue will follow a Poisson distribution with mean 0 rer hour. Obtain the likelihood function for 0 given that the number of arrivals in the last n hours were 21, 12,., Dn. (c) Deduce the posterior distribution of 0 when the prior distribution is Gamma(a, B) and write down its probability density function. (d) Show that the Bayes estimate of 0 under the loss function specified in part (a) is given by a + s - 1 B+n where s = (e) Find the predictive distribution of y the number of arrivals in the next hour.
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