EBK DATA STRUCTURES AND ALGORITHMS IN C
4th Edition
ISBN: 9781285415017
Author: DROZDEK
Publisher: YUZU
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Consider a piece of text in which the letters a,e,g,k,l,z occur with probabilities of 3, 8, 13, 19,23,34 percent. Generate a Huffman table to code them.
Consider values shown in the table below:i=1 (cold) i=2 (allergy) i=3 (stomach pain) p(Hi)0.60.30.1 p(E1 |Hi)0.30.80.3 p(E2 |Hi)0.60.90.0Those values represent (hypothetically) three mutually exclusive and exhaustive hypotheses for the patient’s condition. For example, H1: the patient has a cold, H2: the patient has an allergy, and H3: the patient has stomach pain with their prior probabilities, p(Hi)’s and two conditionally independent pieces of evidence (E1, patient sneezes and E2, patient coughs) which support these hypotheses to differing degrees. Therefore;a) Compute the posterior probabilities for the hypothesis if the patient sneezes. What is the conclusion that can be derived from this condition?b) Based on the answer from the previous result, as the patient coughs are now observed, compute the posterior probabilities for this condition. Explain the results.
Say that you have the following initial settings for binary logistic regression:
x = [1, 1, 3]
w = [0, -2, 0.75]
b = 0.5
2. Given that x's label is 1, what is the value of w_1, w_2, and w_3 at time t + 1 if the learning rate is 1?
For this problem, you may ignore the issue of updating the bias term.
3. What is the value of P(y = 1 | x) given your updated weights from the previous question?
4. Given that x's label is 1, what is the value of the bias term at time t + 1 if the learning rate is 1?
5. What is the value of P(y = 1 | x) given both your updated weights and your updated bias term?
6. Given that x's label is 0, what is the value of P(y = 0| x) at time t + 1 if the learning rate is 0.1?
Round your answer to the nearest 1000th as a number [0, 1].
Chapter 11 Solutions
EBK DATA STRUCTURES AND ALGORITHMS IN C
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