MHB Posterior distribution question Pixie's question from Yahoo Answers

  • Thread starter Thread starter CaptainBlack
  • Start date Start date
  • Tags Tags
    Distribution
Click For Summary
The discussion focuses on calculating the posterior distribution of the success probability alpha in a Bernoulli trial using Bayes' theorem. For the first case, with one success in one trial, the posterior distribution is derived as P(alpha|data) = 2α. In the second case, with two successes in five trials, the likelihood function is represented as P(data|alpha) = b(2;5,alpha) = (5!/(2!3!))α^2(1-α)^3. The prior distribution for alpha is uniform on the interval [0,1]. The calculations demonstrate how observed data influences the posterior distribution of alpha.
CaptainBlack
Messages
801
Reaction score
0
Reposted from Yahoo Answers:

Suppose that Yi is the result of a Bernoulli trial, with probability alpha of success (Yi=1).
If we assign a Unif(0,1) prior distribution to alpha, find the posterior distribution of alpha after the observations:
(a) 1
(b) 0, 1, 1, 0, 0

Thank you so much if you can help! :)
 
Physics news on Phys.org
CaptainBlack said:
Reposted from Yahoo Answers:

Suppose that Yi is the result of a Bernoulli trial, with probability alpha of success (Yi=1).
If we assign a Unif(0,1) prior distribution to alpha, find the posterior distribution of alpha after the observations:
(a) 1
(b) 0, 1, 1, 0, 0

Thank you so much if you can help! :)


From Bayes' theorem we have:\[P(\alpha|data) = \frac{P(data|\alpha)P(\alpha)}{P(data)}\]

For case (a) we have one success in one trial with prob of success \(\alpha\) so:

\(P(data|\alpha)=\alpha \)

\(P(\alpha)=1\) (since the prior for alpha is \(U(0,1)\) its density is \(1\) for \(\alpha\) in \([0,1]\) and zero elswhere)

\( \displaystyle P(data) = \int_{\alpha=0}^1 P(data|\alpha)P(\alpha)\; d \alpha =\int_{\alpha=0}^1 \alpha \; d \alpha =\Bigl[ \alpha^2/2 \Bigr]_0^1=\frac{1}{2} \)

and so:
\[ P(\alpha|data)=2 \alpha\]For case (b) we have 2 successes in 5 trials so:

\( \displaystyle P(data|\alpha)= b(2;5,\alpha)= \frac{5!}{2!\;3! }\alpha^2 (1-\alpha)^3 \)

CB
 
There is a nice little variation of the problem. The host says, after you have chosen the door, that you can change your guess, but to sweeten the deal, he says you can choose the two other doors, if you wish. This proposition is a no brainer, however before you are quick enough to accept it, the host opens one of the two doors and it is empty. In this version you really want to change your pick, but at the same time ask yourself is the host impartial and does that change anything. The host...

Similar threads

  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 9 ·
Replies
9
Views
2K
  • · Replies 14 ·
Replies
14
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
Replies
3
Views
2K
  • · Replies 6 ·
Replies
6
Views
2K
Replies
1
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
Replies
1
Views
2K
Replies
3
Views
3K