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?
X is a discrete random variable with the following PMF: P(X = 1.4) = 0.25 P(X = -9.7) = 0.25 P(X = 10) = 0.5 Find the standard deviation of X.
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Complete questions d and e only Question 1: Consider the data set shown in the following Table: Record A B C 0 0 0 2 0 0 1 3 0 1 1 4 0 1 1 0 1 1 1 1 1 10 1 0 1 a) Estimate the conditional probabilities for P(A+), P(B+), P(CI+), P(A-), P(B|-), and P(C-). b) Use the estimate of conditional probabilities given in the previous question to predict the class label for a test sample (A=0, B=1, C=0) using the naive Bayes approach. c) Estimate the conditional probabilities using the m-estimate approach, with p=1/2 and m=4. d) Repeat part (b) using the conditional probabilities given in part (c). e) Compare the two methods for estimating probabilities. Which method is better and why? Ans a,b,c a) P (A = 1|?) = 2/5 = 0.4, P (B = 11?) = 2/5 = 0.4, P (C = 11?) = 1, P (A= 01?) = 3/5 = 0.6, P (B = 01?) = 3/5 = 0.6, P (C = 0[?) = 0; P (A = 1|+) = 3/5 = 0.6, P (B = 1|+) = 1/5 = 0.2, P (C = 11+) = 4/5 = 0.8, P (A=0|+) = 2/5 = 0.4, P (B = 0+) = 4/5 = 0.8, P(C=0]+)=1/5=0.2 b) Let's assume that P (A = 0, B…
The would not be viewed as having a probability distribution? deterministic factors input factors output variables simulation method
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Consider the joint probability distribution on the right. a) What is P(A = false)? Show your work. b) What is P(A=true or B=true)? Explain. c) What is P(A=true|C=false)? d) Are A and C independent? Why?
3.. ,Given a BSC with p = 0.8 and P = 0.7 (1) Compute the output probability distribution: q, and q1. Note qo = Pr (b = 0) and q1 = Pr (b = 1). %3D (2) Compute the backward probabilities: Qo1: Q11- Note Qij = P(a = i|b = j)
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.
M3
lzw: we make two 1000 letter words with the following probabilities for each letter: A: Pb(a)=0.5, Pb(b)=0.2, Pb(c)=0.15, Pb(d)=0.15 B: Pb(a)=0.3, Pb(b)=0.3, Pb(c)=0.25, Pb(d)=0.15 with the lzw algorithm we get compression ratios: Compression ratio for A will always be smaller than for B (A can be compressed down to less bits than B) Explain why the differance based on given probabilities.

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