We model the following coin toss experiment. Toss a coin n times. Let A1, A2, An that X₂ = 1 if ith coin toss lands on head; otherwise X₂ = 0, i = 1, ..., n. Then each X, is a Bernoulli random variable with distribution function f(X) = px (1 - p)1-X₁, where the unknown parameter p = Pr(X; = 1), i=1,...,n. We aim to make inference of the unknown parameter p, the probability of getting a head, based on the data X₁,..., Xn. a. Find the joint distribution f(X1,..., Xn) of the data X₁,.., Xn. b. Find the log-likelihood function of p based on the result of a. c. Find the maximum likelihood estimator (MLE) of p based on the result of b.
We model the following coin toss experiment. Toss a coin n times. Let A1, A2, An that X₂ = 1 if ith coin toss lands on head; otherwise X₂ = 0, i = 1, ..., n. Then each X, is a Bernoulli random variable with distribution function f(X) = px (1 - p)1-X₁, where the unknown parameter p = Pr(X; = 1), i=1,...,n. We aim to make inference of the unknown parameter p, the probability of getting a head, based on the data X₁,..., Xn. a. Find the joint distribution f(X1,..., Xn) of the data X₁,.., Xn. b. Find the log-likelihood function of p based on the result of a. c. Find the maximum likelihood estimator (MLE) of p based on the result of b.
MATLAB: An Introduction with Applications
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Author:Amos Gilat
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Chapter1: Starting With Matlab
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
Transcribed Image Text:We model the following coin toss experiment. Toss a coin n times. Let X₁, X2, ..., Xn be random variables such
that X; = 1 if ith coin toss lands on head; otherwise X; = 0, i = 1, ..., n. Then each X; is a Bernoulli random
variable with distribution function f(X;) = px (1 - p)1-Xi, where the unknown parameter p = Pr(X; = 1),
i=1,...,n. We aim to make inference of the unknown parameter p, the probability of getting a head, based on
the data X₁,..., Xn.
a. Find the joint distribution f(X1,..., Xn) of the data X₁,.., Xn.
b. Find the log-likelihood function of p based on the result of a.
c. Find the maximum likelihood estimator (MLE) of p based on the result of b.
d. Suppose that a coin was tossed n 20 times and 6 heads were observed. Plug in the data to compute the
MLE of p.
=
e. Based the data in d, find the uncertainty of the MLE.
f. Write down the distribution of MLE when the sample size n is relatively large.
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