Truncated Poisson: Suppose observations come from Poisson(u), but only non-zero values are recorded. The likelihood is L(1) x I Data: 3, 1,2, 4, 2, 1, 3, 1, 2, 1 Prior: p() = 1 (a) Construct a Metropolis-Hasting (M-H) algorithm. Use M-H with proposal distribution q(4p) : N(0 : mean = with burn in phase 1500. Give a 95% confidence interval for u. Hp, std = 2). Set Prob(acceptance) O if u <0. Nunber of MCMC draws 15000

MATLAB: An Introduction with Applications
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Truncated Poisson: Suppose observations come from Poisson(u), but only non-zero values are recorded.
The likelihood is
L(1) x I
Data: 3, 1,2, 4, 2, 1, 3, 1, 2, 1
Prior: p() = 1
(a) Construct a Metropolis Hasting (M-H) algorithm. Use M-H with proposal distribution q(p) :
N(0 : mean =
with burn in phase 1500. Give a 95% confidence interval for p.
Hps std = 2). Set Prob(acceptance) 0 if u < 0. Number of MCMC draws 15000
Transcribed Image Text:Truncated Poisson: Suppose observations come from Poisson(u), but only non-zero values are recorded. The likelihood is L(1) x I Data: 3, 1,2, 4, 2, 1, 3, 1, 2, 1 Prior: p() = 1 (a) Construct a Metropolis Hasting (M-H) algorithm. Use M-H with proposal distribution q(p) : N(0 : mean = with burn in phase 1500. Give a 95% confidence interval for p. Hps std = 2). Set Prob(acceptance) 0 if u < 0. Number of MCMC draws 15000
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