Simplifying the Conditional probability

In summary, the conversation discusses simplifying the equation P(S1 \cap S2 \cap S3 | r) when S2 and S3 are independent of r. They mention a probability law, P((X|Y)|Z) = P(X | (Y \cap Z)), and suggest using usual probability laws with a condition, "| Z," to make progress.
  • #1
bhathi123
1
0
P(S1 [itex]\cap[/itex] S2 [itex]\cap[/itex] S3 | r)
How do I simplfy the above equation if S2 and S3 are independent of r ?
 
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  • #2
I've don't recall seeing a book that gives the probability law
[itex] P((X|Y)|Z) = P(X | (Y \cap Z) ) [/itex]
but I think its true. How to argue it depends on whether you are approaching probability as measure theory or something simpler.

You can also apply the usual probability laws with a condition lilke "[itex]| Z[/itex]" tagged onto every term. For example,
[itex] P(A \cap B) = P(A|B) P(B) [/itex]
so
[itex] P(A \cap B | Z) = P( (A|B)|Z) P(B | Z) [/itex]

See if you can make progress by applying those ideas.
 

What is conditional probability and why is it important in science?

Conditional probability is the likelihood of an event occurring given that another event has already occurred. In science, conditional probability allows us to make more accurate predictions and understand the relationships between different variables.

How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint event (both events occurring) by the probability of the first event. This can be represented as P(A|B) = P(A and B) / P(B).

What is the difference between conditional and unconditional probability?

Unconditional probability is the likelihood of an event occurring without any prior knowledge or conditions. Conditional probability takes into account a specific condition or event that has already occurred and adjusts the probability accordingly.

Can you provide an example of conditional probability in science?

One example of conditional probability in science is in medical testing. The likelihood of a patient having a certain disease may be different depending on whether they have a family history of the disease or not. The conditional probability of having the disease given a positive family history would be different from the unconditional probability of having the disease.

How can conditional probability be used to improve experimental design?

Conditional probability can be used to identify potential confounding variables in an experiment and adjust for them in the design. By understanding the relationships between variables, scientists can control for potential influences and increase the accuracy and reliability of their results.

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