Let X; € {1,2, 3, ...} be the number of days until relapse for patient i who is diagnosed with multiple sclerosis and currently in remission. We model this data using a geometric distribution with pmf iid X1, X2, ..., X, fxp(x | p) = (1 – p)*-'p for 0

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In the attached image, for part 1 solve the posterior by making use of the likelihood property and prior distribution. Thank you

Let X; € {1,2, 3, ...} be the number of days until relapse for
patient i who is diagnosed with multiple sclerosis and currently in remission. We model this
data using a geometric distribution with pmf
iid
X1, X2, ..., X, fx\p(x | p) = (1 – p)*-'p
for 0 <p <1 defined on x E {1, 2, 3, ...} and 0 elsewhere. Here, p is the risk of relapse on
each day.
1. Using a p~ Beta(a, B) prior, derive the posterior density of p, fpjx, (p | X„).
2. Find the posterior mean of p. Find the MLE of p - you do not need to verify that it
is a max.
3. Show that for n >> a + B, the posterior mean is approximately equal to the MLE.
Transcribed Image Text:Let X; € {1,2, 3, ...} be the number of days until relapse for patient i who is diagnosed with multiple sclerosis and currently in remission. We model this data using a geometric distribution with pmf iid X1, X2, ..., X, fx\p(x | p) = (1 – p)*-'p for 0 <p <1 defined on x E {1, 2, 3, ...} and 0 elsewhere. Here, p is the risk of relapse on each day. 1. Using a p~ Beta(a, B) prior, derive the posterior density of p, fpjx, (p | X„). 2. Find the posterior mean of p. Find the MLE of p - you do not need to verify that it is a max. 3. Show that for n >> a + B, the posterior mean is approximately equal to the MLE.
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