We have N measurements collected as i.i.d. observations from a random variable x and the pdf of measurements is in binomial form as p(x[r] | s)=(»)s*In](1 – s)(N-x[n]) for n = 0,..(N-1). x[n], where s denotes the probability of success. a. Write p(x | s) and design a ML (Maximum Likelihood) estimator for s. b. Show that the ML estimator is unbiased. (Hint: E[x [n] ]= Ns for the binomial distribution) c. Calculate the CRLB (Cramer-Rao lower bound).
We have N measurements collected as i.i.d. observations from a random variable x and the pdf of measurements is in binomial form as p(x[r] | s)=(»)s*In](1 – s)(N-x[n]) for n = 0,..(N-1). x[n], where s denotes the probability of success. a. Write p(x | s) and design a ML (Maximum Likelihood) estimator for s. b. Show that the ML estimator is unbiased. (Hint: E[x [n] ]= Ns for the binomial distribution) c. Calculate the CRLB (Cramer-Rao lower bound).
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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