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.
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Q: =1 (cold) i=2 (allergy) i=3 (stomach pain) p(Hi) 0.6 0.3 0.1 p(E1 |Hi) 0.3 0.8 0.3 p(E2 |Hi) 0.6 0.9…
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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.
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- Write a python program to implement Linear Regression algorithm. Use the sample data-set [(1,3) (2,5) (3,7) (4,7) (5,11) (6,13)] to predict the values for X=10, 15, 20, 25. Evaluate the accuracy of the model (R2 value).In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset. 1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 3535Say 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].
- In your own words explain why we would want to evaluate all probabilities.Mention some drawbacks of the Linear Model?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.
- From the following well-formed formula Let P: A ∧ B and Q : A → C P → Q: A ∧ B → If A, Then C P → Q : A ∧ B → (A → C) Construct a table that evaluates the truth value for P → Q Is there a modus ponens? ThanksA simulation experiment consisting of 4 replications returned the 90% confidence interval (54,68) for the monthly return of a financial portfolio (in thousands of dollars). Specify an approximate distribution for the monthly return with appropriate parameters.How can you recognize whether a model satisfies proportionality andadditivity?
- Give typing answer with explanation and conclusion Using knowledge of knock-out options and their pricing via Monte Carlo simulation, demonstrate the effect on options prices when we vary the frequency at which barrier crossing is checked. Using MATLAB and choosing your own options parameters, perform the simulation and show the results to demonstrate the effect?Make a Venn diagram showing the relationships among test cases from boundary value analysis, robustness testing, worst-case testing, and robust worst-case testing.Give a training example (consisting of values for α, ω and the relevance judgment) that when added to the training set makes it impossible to separate the Rs from the Ns using a line in the α–ω pla