For this problem the observation Y = (Y1,Y2) is a vector of two random variables Y1,Y2. The hypotheses are as follows
H0 : Y1 N(3,1),Y2 N(1,1) H1 : Y1,Y2 Px,
where N(,2) is the Gaussian distribution with mean and variance 2, and Px is the distribution with the following probability density function
, for all x R.
In both the hypotheses, the random variables Y1 and Y2 are independent of each other. Use uniform cost, i.e., C00 = C11 = 0, C10 = C01 = 1.
You must use Monte Carlo simulation to find and plot V (0) versus 0, for 0 = 0.1,0.2,,0.9.
Simulation Details:
- For each value 0, you must generate 106 instances of Y and apply the Bayes decision rule to perform detection. Use the average cost of these 106 instances as your estimate for V (0).
- In each instance, you must randomly generate Y according to either H0 (with probability 0) or H1 (with probability 1 0).
- Ensure that the observation vectors generated for H0 and H1 indeed satisfy the probability distributions described above.
- Reference value: at 0 = 0.25, V 0.

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