Conditional independence problem

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SUMMARY

The discussion centers on the conditional independence of events A1 and A2 given event B, specifically addressing the statement that if A1 and A2 are conditionally independent given B, they are also conditionally independent of the complement of B. A counterexample is provided using a probability space defined by Ω = {a, b, c} with uniform probabilities. The events are defined as A1 = {a}, A2 = {a, b}, and B = {c}, demonstrating that the initial statement is false.

PREREQUISITES
  • Understanding of conditional independence in probability theory
  • Familiarity with probability spaces and events
  • Knowledge of basic probability concepts such as complements and intersections
  • Ability to interpret and manipulate probability distributions
NEXT STEPS
  • Study the concept of conditional independence in depth using "Probability Theory: The Logic of Science" by E.T. Jaynes
  • Explore counterexamples in conditional independence through "A First Course in Probability" by Sheldon Ross
  • Learn about Bayesian networks and their implications for conditional independence
  • Investigate the implications of conditional independence in machine learning models, particularly in graphical models
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Students and professionals in statistics, data science, and machine learning who need to understand the implications of conditional independence in probabilistic models.

bl00d1
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Let A1, A2 and B be events with P(B)>0. Events A1 and A2 are said to be conditionally independent given B if P(A1nA2|B)=P(A1|B)P(A2|B).

Prove or disprove the following statement:

Suppose 0<P(B)<1. If events A1 and A2 are conditionally independent then A1 and A2 are also condtionally independent of Bcomplement.
 
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Re: conditional independence problem

bl00d said:
where?

The figure with the squares. If you prefer, consider $\Omega=\{a,b,c\}$ and the probability $p$ on $\mathcal{P}(\Omega)$ defined by $p(a)=p(b)=p(c)=1/3$, and choose $A_1=\{a\}$, $A_2=\{a,b\}$ and $B=\{c\}$. Let's see what do you obtain.
 
Re: conditional independence problem

Fernando Revilla said:
The figure with the squares. If you prefer, consider $\Omega=\{a,b,c\}$ and the probability $p$ on $\mathcal{P}(\Omega)$ defined by $p(a)=p(b)=p(c)=1/3$, and choose $A_1=\{a\}$, $A_2=\{a,b\}$ and $B=\{c\}$. Let's see what do you obtain.

oh gosh.. it's right in front of me and i didnt see it
 

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