1. We consider a Bayesian network with 20 random variables. We only know that 10 of the variables are isolated from the other 10. What are the number of parameters required to represent the joint distribution? a. 2^10-1 b. 2(2^10-1) c. 2*10 d. 2^20-1
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1. We consider a Bayesian network with 20 random variables. We only know that 10 of the variables are isolated from the other 10. What are the number of parameters required to represent the joint distribution?
a. 2^10-1
b. 2(2^10-1)
c. 2*10
d. 2^20-1
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Solved in 2 steps
- Consider the following procedure for initializing the parameters of a neural network: 1. Pick a random number r r = rand(1,1) * (2 + INIT_EPSILON ) – INIT_EPSILON 2. Set e =r for all i, j,l Does this work? No, because the procedure fails to break symmetry. O b. Yes, unless we are unlucky and get r = 0 (up to numerical precision). O. Yes or no, depending on the training set inputs x(i). d. Yes, because the parameters are chosen randomly.Using Matlab, plot the following ultrasound pressure wave as a function of x for t =1 P(t, x) = Poe** cos(@t – kx) where a = 0.15 neper/m, where neper is a dimensionless quantity, k = 1 rad/cm, o = 1 rad/s, and Po = 15 N/m2. Submit your ".m" file as well as the resulting graph labeled appropriately.a. Write down the factorization of P(D, I, G, S, L) based on the Bayesian network.
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