Showing Conditional Independence Does Not Imply Independence

In summary, the conversation discusses the concept of conditional independence and its relation to independence. The speaker uses the example of coin tosses and basketball shots to demonstrate that independence does not imply conditional independence, and vice versa. They suggest finding probabilities and proving that they satisfy the condition for conditional independence.
  • #1
Jason4
28
0
I know this isn't quite advanced probability, but I'm not sure if I have this right.

I want to show that conditional independence of $X$ and $Y$ given $Z$ does not imply independence of $X$ and $Y$ (and vice versa).

So I used coin tosses where:

$X=\{$ first coin tails $\}$

$Y=\{$ second coin tails $\}$

$Z=\{$ both coins same $\}$

I can show that independence does not imply conditional independence.

How do I show that conditional independence does not imply independence?
 
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  • #2
Well to answer such a question, you need to take two events that are not independent and show they are conditionally independent given a third event. Example:A basketball player has two shots.Let A be the event that the player scores the first shot. Assume P(A) = 0.3Let B be the event that the player scores the second shot. Assume P(B/A) = 0.2 and P(B/A' ) = 0.4 (if he/she scores the first shot, he/she has less probability of scoring the second)Let C be the event that both shot are scored. Clearly, A and B are not independent. Try to find P(A/C) and P(B/C) and P( (A and B)/C) and prove that P(A/C)*P(B/C) = P( (A and B) /C).
 

FAQ: Showing Conditional Independence Does Not Imply Independence

What is conditional independence?

Conditional independence is a relationship between two events or variables where the occurrence or value of one event or variable does not affect the probability of the other event or variable occurring or having a certain value.

What does it mean to show that conditional independence does not imply independence?

This means that even though two events or variables may be conditionally independent, they may still be dependent in some way. In other words, the conditional independence relationship does not necessarily imply a complete absence of any relationship between the two events or variables.

Why is it important to understand that conditional independence does not imply independence?

It is important because assuming that two events or variables are independent solely based on their conditional independence can lead to incorrect conclusions and decisions. It is important to properly assess the relationship between events or variables to make accurate predictions or decisions.

How can we show that conditional independence does not imply independence?

This can be shown through various statistical techniques such as hypothesis testing, correlation analysis, and regression analysis. These methods can help determine the strength and nature of the relationship between two events or variables, even if they are conditionally independent.

Can we ever assume independence based on conditional independence?

No, we cannot assume independence solely based on conditional independence. It is important to always consider other factors and use appropriate statistical methods to assess the relationship between events or variables before making any assumptions about independence.

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