Fall 23 HW3.1. Marietta Traffic Authority is concerned about the repeated accidents at the intersection of Canton and Piedmont Roads. Bayes-inclined city-engineer would like to estimate the accident rate, even better, find a credible set. A well known model for modeling the number of road accidents in a particular loca- tion/time window is the Poisson distribution. Assume that X represents the number of accidents in a 3 month period at the intersection od Canton and Piedmont Roads. Assume that [X|0] ~ Poi(0). Nothing is known a priori about 0, so it is reasonable to assume the Jeffreys' prior 7(0) = √1(0 < 0 < ∞). In the four most recent three-month periods the following realizations for X are observed: 1, 2, 0, and 2. (a) Compare the Bayes estimator for with the MLE (For Poisson, recall, ÎMLE = X). (b) Compute (numerically) a 95% equitailed credible set. (c) Compute (numerically) a 95% HPD credible set. (d) Numerically find the mode of the posterior, that is, MAP estimator of 0. (e) If you test the hypotheses Ho: 021 H₁ : 0 <1, based on the posterior, which hypothesis will be favored? US

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Fall 23 HW3.1. Marietta Traffic Authority is concerned about the repeated accidents at
the intersection of Canton and Piedmont Roads. Bayes-inclined city-engineer would like to
estimate the accident rate, even better, find a credible set.
A well known model for modeling the number of road accidents in a particular loca-
tion/time window is the Poisson distribution. Assume that X represents the number of
accidents in a 3 month period at the intersection od Canton and Piedmont Roads.
Assume that [X|0] ~ Poi(0). Nothing is known a priori about 0, so it is reasonable to
assume the Jeffreys' prior
T(0)
1(0 <0<∞).
In the four most recent three-month periods the following realizations for X are observed:
1, 2, 0, and 2.
=
(a) Compare the Bayes estimator for with the MLE (For Poisson, recall, MLE = X).
(b) Compute (numerically) a 95% equitailed credible set.
(c) Compute (numerically) a 95% HPD credible set.
(d) Numerically find the mode of the posterior, that is, MAP estimator of 0.
(e) If you test the hypotheses
Ho: 021 US
H₁ : 0 <1,
based on the posterior, which hypothesis will be favored?
Transcribed Image Text:Fall 23 HW3.1. Marietta Traffic Authority is concerned about the repeated accidents at the intersection of Canton and Piedmont Roads. Bayes-inclined city-engineer would like to estimate the accident rate, even better, find a credible set. A well known model for modeling the number of road accidents in a particular loca- tion/time window is the Poisson distribution. Assume that X represents the number of accidents in a 3 month period at the intersection od Canton and Piedmont Roads. Assume that [X|0] ~ Poi(0). Nothing is known a priori about 0, so it is reasonable to assume the Jeffreys' prior T(0) 1(0 <0<∞). In the four most recent three-month periods the following realizations for X are observed: 1, 2, 0, and 2. = (a) Compare the Bayes estimator for with the MLE (For Poisson, recall, MLE = X). (b) Compute (numerically) a 95% equitailed credible set. (c) Compute (numerically) a 95% HPD credible set. (d) Numerically find the mode of the posterior, that is, MAP estimator of 0. (e) If you test the hypotheses Ho: 021 US H₁ : 0 <1, based on the posterior, which hypothesis will be favored?
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