Understanding Joint Probability with No-Replacement Rule

In summary, the order of events does not matter in determining joint probability P(A.B) as long as there is no natural temporal ordering between the events. However, in cases where there is a natural temporal ordering, the order of events may affect the probability. In conditional probability cases, such as P(R|B), the order in which events occur is important, but this does not conflict with the definition of joint probability using conditional probabilities as long as the no-replacement rule is taken into account.
  • #1
sauravrt
15
0
If A and B are two events and I want to look at their joint probability P(A.B) do I have to be concerned with the order in with A and B occur?

Saurav
 
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  • #2
No, in general. However, consider:

A = {accumulation > x}

B = {accumulation < x}.

In this case, there is a natural temporal ordering of the events A and B; B almost certainly occurs before A.
 
  • #3
Thanks EnumaElish

So now if I am looking at a problem where I have 3 red balls and 4 blue balls and if I pickup two balls want to find the probability P(R|B) (i.e prob of picking Red ball given Blue ball was already picked). In this conditional probability case, the order in which the balls were picked is important, am i correct? However the joint probability P(RB) = P(R|B).P(B) = P(B|R).P(R) is not concerned with the order in which the balls were picked?

Saurav
 
  • #4
You are correct that as long as you are not replacing each draw (by putting a drawn ball back into the bin), the order of draws will matter. This does not have to conflict with the definition of joint probability using conditional probabilities as long as you define each event by taking the no-replacement rule into account. So if you define P(R|B) as the probability of drawing a red having drawn a blue, then P(RB) will be defined accordingly.
 

What is joint probability?

Joint probability is the probability of two or more events occurring together. It is a measure of the likelihood of the intersection of two or more events happening simultaneously.

How is joint probability calculated?

Joint probability is calculated by multiplying the individual probabilities of each event. For example, if event A has a probability of 0.5 and event B has a probability of 0.7, the joint probability of both events occurring is 0.5 * 0.7 = 0.35.

What is the difference between joint probability and conditional probability?

Joint probability is the probability of two or more events occurring together, while conditional probability is the probability of one event occurring given that another event has already occurred. Conditional probability takes into account prior knowledge about the occurrence of one event when calculating the probability of another event.

What is the relationship between joint probability and marginal probability?

Joint probability and marginal probability are related in that joint probability is the product of the marginal probabilities of each event. Marginal probability is the probability of a single event occurring, without taking into account any other events. In other words, joint probability is a combination of multiple marginal probabilities.

What are some real-life applications of joint probability?

Joint probability is commonly used in fields such as statistics, actuarial science, and machine learning to model and predict the likelihood of multiple events occurring together. It can also be applied in risk assessment and insurance pricing, as well as in medical research to understand the probability of multiple factors contributing to a disease or condition.

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