How to Compute Conditional Probability Without Deriving f(y)

In summary, the conversation involves a problem where the goal is to compute the probability that a point (x,y) lies above the line x=y. The first step of the problem is to derive the distribution f(x,y) using the given f(x) and f(y|x), and the second step is to compute P(y>x). The difficulty lies in deriving f(y) in order to evaluate the points above the line x=y. However, the function f(x,y) is used instead of f(y) as it gives the distribution for a point (x,y).
  • #1
BookMark440
10
0
I'm stuck on a problem. I was given f(x) and f(y|x) and was able to derive f(x,y). The second step of the problem is computing P[y>x]. I think I need to know f(y) to answer this problem but I can't figure out how to derive it. Or is there a way to compute P(y>x) given the info I know without deriving f(y)?

THANKS!
 
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  • #2
P(y>x) is simply the probability that a point (x,y), lies above the line x=y. Do you remember how to do double integrals between curves?
 
  • #3
I understand that part. My problem (I think) is that I need to evaluate when f(y) has points above the line x=y and I do not know how to derive f(y). Does that make any sense?
 
  • #4
BookMark440 said:
I understand that part. My problem (I think) is that I need to evaluate when f(y) has points above the line x=y and I do not know how to derive f(y). Does that make any sense?

You use f(x,y) not f(y) because it is the function f(x,y) that is the distribution for a point (x,y). f(y) only gives the distribution for y.
 

What is conditional probability?

Conditional probability is a mathematical concept used to calculate the likelihood of an event occurring given that another event has already happened. In other words, it is the probability of an event A occurring, given that event B has already occurred.

How is conditional probability calculated?

The formula for calculating conditional probability is P(A|B) = P(A and B) / P(B), where P(A|B) represents the probability of event A occurring given that event B has already occurred, P(A and B) represents the probability of both events A and B occurring, and P(B) represents the probability of event B occurring. This formula can be used to calculate the conditional probability of any two events.

What is the difference between conditional probability and joint probability?

Conditional probability and joint probability are two different concepts, but they are related. Joint probability refers to the likelihood of two events occurring together, while conditional probability refers to the likelihood of one event occurring given that another event has already occurred. In other words, joint probability considers two events at the same time, while conditional probability takes into account the occurrence of one event before the other.

How is conditional probability used in real-life situations?

Conditional probability is used in various fields such as statistics, machine learning, and economics to make predictions and analyze data. It is also commonly used in risk assessment, insurance, and medical diagnosis. For example, doctors may use conditional probability to determine the likelihood of a patient having a certain disease given their symptoms and medical history.

What are some common misconceptions about conditional probability?

One common misconception about conditional probability is that it is the same as independent probability. In reality, conditional probability takes into account the relationship between two events, while independent probability assumes that the two events have no influence on each other. Another misconception is that the order of events does not matter in conditional probability, when in fact it can significantly impact the calculation.

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