Your friend transmits an unknown value to you over a noisy channel. The noise is normally distributed with mean 0 and a known variance 4, so the value that you receive is modeled by N (0,4). Based on previous communications, your prior on is N (5,9). Suppose your friend transmits a value to you that you receive as a = 6. Show that the posterior pdf for 0 is N(74/13, 36/13). For this problem, you need to derive the posterior by carrying out the calculations from scratch. Suppose your friend transmits the same value to you n = 4 times. You receive these signals plus noise as ₁,..., ₁ with sample mean = 6. Using the same prior and known variance o² as in part (a), show that the posterior on 0 is N(5.9,0.9). Plot the posterior and posterior on the same graph. Describe how the data changes your belief about the true value of 0. For this question, you may use the normal updating formulas. (c) How do the mean and variance of the posterior change as more data is received?

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part c please 

Your friend transmits an unknown value to you over a noisy channel. The
noise is normally distributed with mean 0 and a known variance 4, so the value x that
you receive is modeled by N (0,4). Based on previous communications, your prior on
is N (5,9).
Suppose your friend transmits a value to you that you receive as x = 6. Show that
the posterior pdf for eis N(74/13, 36/13). For this problem, you need to derive
the posterior by carrying out the calculations from scratch.
Suppose your friend transmits the same value to you n = 4 times. You receive
these signals plus noise as ₁,..., 4 with sample mean = 6. Using the same prior
and known variance o² as in part (a), show that the posterior on is N(5.9, 0.9).
Plot the posterior and posterior on the same graph. Describe how the data changes
your belief about the true value of 0. For this question, you may use the normal
updating formulas.
How do the mean and variance of the posterior change as more data is received?
Transcribed Image Text:Your friend transmits an unknown value to you over a noisy channel. The noise is normally distributed with mean 0 and a known variance 4, so the value x that you receive is modeled by N (0,4). Based on previous communications, your prior on is N (5,9). Suppose your friend transmits a value to you that you receive as x = 6. Show that the posterior pdf for eis N(74/13, 36/13). For this problem, you need to derive the posterior by carrying out the calculations from scratch. Suppose your friend transmits the same value to you n = 4 times. You receive these signals plus noise as ₁,..., 4 with sample mean = 6. Using the same prior and known variance o² as in part (a), show that the posterior on is N(5.9, 0.9). Plot the posterior and posterior on the same graph. Describe how the data changes your belief about the true value of 0. For this question, you may use the normal updating formulas. How do the mean and variance of the posterior change as more data is received?
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