# Suppose we know that the ten data points in category ω2 in the table above come from a three-dimensional uniform distribution  Suppose, however, that we do not have access to the x3 components for the even-numbered data points. (a) Write an EM program to estimate the six scalars comprising xl and xu of the distribution. Start your estimate with (b) Compare your final esimate with that for the case when there is no missing data. Write a program to evaluate the Bayesian belief net for fish in Example 3, including the information in  Test your program on the calculation given in the Example. Apply your program to the following cases, and state any assumptions you need to make. (a) A dark, thin fish is caught in the north Atlantic in summer. What is the probability it is a salmon? (b) A thin, medium fish is caught in the north Atlantic. What is the probability it is winter? spring? summer? autumn? (c) A light, wide fish is caught in the autumn. What is the probability it came from the north Atlantic?

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Suppose we know that the ten data points in category ω2 in the table above come from a three-dimensional uniform distribution  Suppose, however, that we do not have access to the x3 components for the even-numbered data points.

(a) Write an EM program to estimate the six scalars comprising xl and xu of the distribution. Start your estimate with

(b) Compare your final esimate with that for the case when there is no missing data.

Write a program to evaluate the Bayesian belief net for fish in Example 3, including the information in  Test your program on the calculation given in the Example. Apply your program to the following cases, and state any assumptions you need to make.

(a) A dark, thin fish is caught in the north Atlantic in summer. What is the probability it is a salmon?

(b) A thin, medium fish is caught in the north Atlantic. What is the probability it is winter? spring? summer? autumn?

(c) A light, wide fish is caught in the autumn. What is the probability it came from the north Atlantic?