We consider a Bayesian network with 74 variables including Variables A, B, and C. If we want to study inference P(A|B,C), how many hidden variables are there?
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- Simulate a probability curve fx (x)~N(µ,δ) in Matlab AND Calculate the cumulative probability fx(x) Note: you are free to choose the example or the data for your case study.Implement a simple linear regression model using Python without using any machine learning libraries like scikit-learn. Your model should take a dataset of input features X and corresponding target values y, and it should output the coefficients w and b for the linear equation y =wX + bDraw the probability tree for the situation. Draw the reverse tree for the situation.
- Give an example of a random variable X : {b, c, d, e} → N (Natural Number) with expectation 2, where each of {b, c, d, e} has equal probability1. The impulse response of a causal system is: h(t) = A cos(wt) e¯¹/¹u(t) where u(t) is the Heaviside step function. The response is measured experimentally with a sampling interval of T. a. Write an expression for the sampled impulse response h[n]. b. Calculate the z transform of h[n] and write an expression for H[z]. Use the tables provided below as necessary. c. Does the system have an infinite impulse response (IIR) or finite impulse response (FIR)? Justify your answer. d. What is the DC gain of H[z]? e. Write a difference equation that describes the output y[n] in terms of input x[n].If there are n propositional variables in a propositional expression, what is a function f(n) that yields the number of rows that we need in a truth table to fully examine all possible outcomes of that expression? Why?
- Show validity by natural deduction: ∀xp(x, f(x)) → ∀x∃yp(x,y)Simulate a probability curve fx(x)~N(µ,δ) in Matlab AND Calculate the cumulative probability fx (x)note: attach matlab code of the simulation, may be in textshows a factor r(X,Y, Z) from a 3-variable probabilistic inference function. Show how to eliminate the variable Y from the factor by summing out method (show all of its conversion steps). X Y Z val t t t 0.1 t f0.9 t f t r(X,Y,Z) =| t f f0.8 f t f tf f t f f f0.7 t 0.2 t 0.4 0.6 0.3
- Probability Consider a probability experiment with S = {0, 1, 2, 3, 4, 5, 6, 7} and these two events: A = {1,2, 3}, and B= {1,3, 5, 6, 7} What is the probability that A occurs? What is the probability that B occurs? What is the obability that A n B occurs? What is the probability that AU Boccurs? What is the probability that {} occurs? What is the probability that S occurs?Four different machine learning algorithms are shown in this section, which may be used for supervised learning on a given dataset. Give a description of any four variables you may use to help you choose which one to utilize to determine whether or not a tumor is malignant.Correct answer will be upvoted else downvoted. Computer science. With likelihood 12, he leaves his present club, then, at that point, makes another club himself and goes along with it. There is just a single understudy (himself) in the new club he makes. With likelihood 12, he doesn't make new clubs. For this situation, he changes his club to another one (perhaps a similar club he is in right now) with likelihood relative to the number of understudies in it. Officially, assume there are k clubs and there are bi understudies in the I-th club for 1≤i≤k (before the understudy blows up). He leaves his present club, and afterward joins the I-th club with likelihood canister. We note that when a club becomes unfilled, understudies won't ever go along with this is on the grounds that any understudy who blows up will get a vacant club together with likelihood 0 as per the above assertion. Homer ponders the normal number of days until each understudy is in a similar club…