Using the Bayesian Network shown below:P(B=true|A)Atrue0.2false0.4BP(A=true)A0.4CAP(C=true|A)true0.75false0.5Calculate the conditional probability: P (A=false | B=true, C=false)Show your calculations.8

Step 1: Calculate P(B=true, C=false | A=false).Based on our final Bayes's Equation, we can see the…

Answered: Using the Bayesian Network shown below:…

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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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.
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?
Consider the Bayesian network below. Assume that each variable is boolean, and that a conditional probability table is specified for each node. What is P(I=t|W=f)? t= true f= false C I=t I=f Select one: a. 0.93 P(H=t| I) P(H=f | I) 0.8 0.2 0.1 0.9 b. 0.75 C. e. 0.17 d. 0.34 Holmes Crash 0.68 Icy Watson Crash P(I=t)| P(I=f) 0.7 0.3 I=t I=f |P(W=t| I) P(W=f | I) 0.8 0.2 0.1 0.9 3
What is conditional probability? If we know that P (A) = 1/4, that P(B) = 1/2, and that P(B|A) = 1/2, what is P (A|B) ? %3D
5 Consider a random variable X which takes values in X= {1, 2, 7} with probabilities as the following table indicates. Consider the two codes wi and w2 defined by the table below X₁ P(X) wi(x) w2(x) X₁ 0.08 1001 010 X₂ 0.21 01 011 X3 0.14 001 000 X₁ 0.04 1000 110 ii. The average codeword length for each code. L(wi) bit/symbol Xs 0.25 11 111 Answer the following: i. Express (into original symbols Xi) the encoded string {000011010111101} using code wi(x). L(w2) bit/symbol X6 0.11 000 101 iii. Explain which code is better for compression applications, and why? X1 0.17 101 001
LZW algorithm 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: A: 8000/2689*100 = 33% B: 8000/2890*100 = 36% Explain what could be the reason behind the differences of compression ratios.
aaba What are the lower and higher bounds of the final interval in arithmetic coding? You can assume that the probability distribution of alb is 0.4, 0.6. If you use E1/E2 scaling, then what are the scaling operations when encoding the sequence.
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Figure1 shows the factor from a probabilistic network. Show how to eliminate the variable 'Fire ' from the factor (show all of your steps). Tampering Fire |Alarm Value f f f f f f 0.43 0.59 f 0.35 t 0.07 0.46 0.64 f f 0.08 t t 0.14
This question refers to the following Bayesian Network P (A = true) = 0.6 A A B false false false true true false true true P (C=true | A, B) 0.3 0.2 0.4 0.6 P (B= true) = 0.5 с B P(A = false, C = False, D = true) D O P(A = false | C = False, D = true) A false true P (B=true | A) 0.2 0.6 B C false false false true true false true true Then the expression (1-0.9)(1-0.2)(0.2)(0.5)+(1-0.4)(1-0.3)(1-0.2)(0.5) corresponds to the value of P (D = false | B, C) 0.4 0.2 0.9 0.1
Given: Sudoku. Solve the values in Column G (7th column from left). Give the value in cell G8 (2nd from top of column G) after applying the probability for the possible value. 1 6 2 84 16 3 6 07 08 3827 58 3 8 86 1 9 4 9 5 2 78 26 3 4 3 1
Consider a simple symmetric (four possible directions are equally likely) 2D random walk with the starting point of (0,0), step length=1. 1. Plot a 1000-step random walk path 2. Every time you repeat question 1, you will go through a different path, and end up with a different final location. Calculate the average distance you would cover, and the average final location you would get

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Using the Bayesian Network shown below: P(B=true|A) A true 0.2 false 0.4 B P(A=true) A 0.4 C A P(C=true|A) true 0.75 false 0.5 Calculate the conditional probability: P (A=false | B=true, C=false) Show your calculations. 8