Consider the graph of a Bayesian Network shown below showing 7 binary random variables.AgeGenderExposureto ToxicsSerumCalciumCancerSmokingLungtumorHow many probabilities will be needed to complete the conditional probability table of variable "Cancer"?How many probabilities will be needed to complete the conditional probability table of variable "Lung tumor"?How many probabilities will be needed in total to complete all the conditional probability tables of this BN?How many probabilities will be needed to complete the joint probability table of all variables?Give the answers for each question as a number (e.g., 4)

Bayesian networks are graphical models that represent probabilistic relationships among a set of…

Answered: Consider the graph of a Bayesian…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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For a HMM, the hidden states are {bull, bear}, the observation variables are {rise, fall}, the initial state probability distribution is [0.5 0.5]¹, the transition probability distribution A is [0.4 0.7; 0.6 0.3], and the observation probability distribution B is [0.8 0.1;0.2 0.9]. If the observation sequence is {fall fall rise}, please show the computation procedure for estimating the most likely state sequence?
The would not be viewed as having a probability distribution? deterministic factors input factors output variables simulation method
2. Take a bivariate normal distribution with two random variables X and Y, with mean value = (1, -1), var(X) = 3, var(Y) = 6, and cor(X,Y) = -0.5. %3! (a) create a contour plot for this data (b) plot 1,000 simulations of this distribution (c) Using 1,000,000 simulations, find (1) the expected value of Y (ii) the expected value of Y, given that X> 2 (ii) the expected value of Y, given that X = 2
dont reject the questions give the steps by steps
What are the most common approaches of probability sampling or nonprobability sampling and what is the main difference between probability sampling or nonprobability sampling.
20 observations in the table below are used to train a Naïve Bayes model, and then a prediction is made to a new observation X(C, S) having X1=C and X2=S. Obs 14 2- 3 45 6- 7- 8- 9 10 11 12 13 14 15 16 17 18 19 20 X1e A A A A A B B- B- B Be Ce Ce Ce Ce Ce De DE De DE De X2e se M M S se se M M Le Le Le Me M L- Le se Me Le se Me Ye 04 0- 1e 14 04 04 0 1e 14 1e 1e 1e 1e 1e 0e 0 1e 1e 14 0eE (a) By applying Laplace Smoothing with Laplace-k estimate k=2, calculate the following: - P(Y=1) = ? P(X1=A|Y=1) = ? P(X1=A|Y=0) = ? P(X2=S|Y=1) = ? P(X2=M[Y=0) = ? P(Y=0) = ? P(X1=B|Y=1) = ? P(X1=C|Y=1) =? P(X1=B|Y=0) = ? P(X1=C[Y=0) = ? P(X2=M|Y=1) = ? P(X2=L[Y=1) =? P(X2=L[Y=0) = ?e P(X1=D[Y=1) = ?« P(X1=D|Y=0) = ?« P(X2=S|Y=0) = ?H (b) Does the new observation X(C, S) belong to class Y=0 or Y=1? You must show your working steps in answering this question.

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