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1. Suppose that a set of samples x1, x2, ..., xn, all real numbers, are drawn i.i.d. from the same distribution. Also assume that this distribution is a Gaussian distribution, which can be represented as N(µ, o²). Write a function that accepts a set of samples and returns the MLE estimator for µ. NOTE: The code below will be evaluated by a Python 2.7 interpreter. 1 def mle(samples): pass Run Reset Once your function is correct, your will receive a submission code that you should input into the answer field. Enter answer here 2. In the previous question, you were asked to write a function for an estimator of a parameter of a distribution. Is the result of this function, an estimator, a random variable? Yes No

1. Suppose that a set of samples x1, x2, ..., xn, all real numbers, are drawn i.i.d. from the same distribution. Also assume that this distribution is a Gaussian distribution, which can be represented as N(µ, o²). Write a function that accepts a set of samples and returns the MLE estimator for µ. NOTE: The code below will be evaluated by a Python 2.7 interpreter. 1 def mle(samples): pass Run Reset Once your function is correct, your will receive a submission code that you should input into the answer field. Enter answer here 2. In the previous question, you were asked to write a function for an estimator of a parameter of a distribution. Is the result of this function, an estimator, a random variable? Yes No

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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1. Suppose that a set of samples x1, x2, ..., xn, all real numbers, are drawn i.i.d. from the same distribution. Also assume that this distribution is a Gaussian distribution, which can be represented as N(u, o²). Write a function that accepts a set of samples and returns the MLE estimator for u. NOTE: The code below will be evaluated by a Python 2.7 interpreter. def mle(samples): pass Run Reset Once your function is correct, your will receive a submission code that you should input into the answer field. Enter answer here 2. In the previous question, you were asked to write a function for an estimator of a parameter of a distribution. Is the result of this function, an estimator, a random variable? Yes No
Transcribed Image Text:1. Suppose that a set of samples x1, x2, ..., xn, all real numbers, are drawn i.i.d. from the same distribution. Also assume that this distribution is a Gaussian distribution, which can be represented as N(u, o²). Write a function that accepts a set of samples and returns the MLE estimator for u. NOTE: The code below will be evaluated by a Python 2.7 interpreter. def mle(samples): pass Run Reset Once your function is correct, your will receive a submission code that you should input into the answer field. Enter answer here 2. In the previous question, you were asked to write a function for an estimator of a parameter of a distribution. Is the result of this function, an estimator, a random variable? Yes No
3. When computing an estimator, it is important to understand that an estimator can be biased. What is the definition of bias in an estimator? The bias is computed by finding two standard deviations of the estimator. The ratio of the actual value to the expected value of the estimator. The difference between the expected value of the estimator and the actual value. The bias is equal to the value of the estimator. The true parameter is assumed to be 0. 4. Using the definition of bias and the formula for the estimator of mu from Question 1, is the estimator of mu a biased estimator? No Yes 5. The bias is also: The expected value of the error of the estimator. The skew of the error of the estimator. The standard deviation of the error of the estimator. The variance of the error of the estimator. O O
Transcribed Image Text:3. When computing an estimator, it is important to understand that an estimator can be biased. What is the definition of bias in an estimator? The bias is computed by finding two standard deviations of the estimator. The ratio of the actual value to the expected value of the estimator. The difference between the expected value of the estimator and the actual value. The bias is equal to the value of the estimator. The true parameter is assumed to be 0. 4. Using the definition of bias and the formula for the estimator of mu from Question 1, is the estimator of mu a biased estimator? No Yes 5. The bias is also: The expected value of the error of the estimator. The skew of the error of the estimator. The standard deviation of the error of the estimator. The variance of the error of the estimator. O O
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