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Y|0~f(yle) = I(y E {1,...,0}), i.e., the data Y is 0(1+0) 10) that has the unknown parameter 8. The possible valu vhere both values have the same prior probability. Suppe ind the posterior PMF of using a Bayesian update table in your calculations and answers.

Y|0~f(yle) = I(y E {1,...,0}), i.e., the data Y is 0(1+0) 10) that has the unknown parameter 8. The possible valu vhere both values have the same prior probability. Suppe ind the posterior PMF of using a Bayesian update table in your calculations and answers.

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter13: Probability And Calculus
Section13.2: Expected Value And Variance Of Continuous Random Variables
Problem 10E
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Suppose Y|8~f(y10) = 0(1+0) -I(y E {1,...,0}), i.e., the data Y is from a distribution with PMF f(y10) that has the unknown parameter 8. The possible values of 8 are 0₁ = 4 and 0₂ = 5, where both values have the same prior probability. Suppose the observed data is Y = 5. Find the posterior PMF of using a Bayesian update table. Note: Do not found any numbers in your calculations and answers.
Transcribed Image Text:Suppose Y|8~f(y10) = 0(1+0) -I(y E {1,...,0}), i.e., the data Y is from a distribution with PMF f(y10) that has the unknown parameter 8. The possible values of 8 are 0₁ = 4 and 0₂ = 5, where both values have the same prior probability. Suppose the observed data is Y = 5. Find the posterior PMF of using a Bayesian update table. Note: Do not found any numbers in your calculations and answers.
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