How do I compute P(y>x) given f(x) and f(y|x)?

In summary, to solve the given problem, we are asked to compute the density of (x,y), E(y), and P(y>x). To do so, we are given f(x) = e^-x and f(y|x) = 1/x e^(-y/x). Using these equations, we can derive f(x,y) and compute E(y). To find P(y>x), we need to find the portion of y where y>x, which can be done by finding the intersection of f(x) and f(y|x) and solving for x. This yields the value (1 - log e) as the point of intersection.
  • #1
BookMark440
10
0

Homework Statement


Given f(x) = e^-x and f(y|x) = 1/x e^(-y/x). Three parts: (a) Compute density of (x,y), (b) Compute E(y) and (c) Compute P(y>x).


Homework Equations


f(x,y) = f(y|x)f(x)
if f(x) = ve^(-vx), then E(x)=v^(-1)

The Attempt at a Solution



I'm stuck on a problem. I was given f(x) and f(y|x) and was able to derive f(x,y) and compute E(y). The third 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)?
 
Physics news on Phys.org
  • #2
Hi BookMark440! :smile:

Hint: what is P(y>x) for a fixed value of x? :wink:
 
  • #3
Maybe I am looking for the wrong answer. Do I simply need to find the portion of f(x)=exp(-x) that are to the left of its intersection with f(x)=x ?

This would mean I am looking for the intersection, which is exp(-x) = x, solving for x?

That is:

-x*log e = log x
0.4343 = -(log x)/x

But what step is next? Is there some law of logs I am missing to simplify this? From graphing the problem, it looks like the point of intersection is (1 - log e) but I can't see how the above translate into (1-log e).
 
Last edited:
  • #4
BookMark440 said:
Maybe I am looking for the wrong answer. Do I simply need to find the portion of f(x)=exp(-x) that are to the left of its intersection with f(x)=x ?

No, for P(y>x) you need to find the portion of y, don't you? :smile:
 

FAQ: How do I compute P(y>x) given f(x) and f(y|x)?

1. What is conditional probability?

Conditional probability is a mathematical concept that measures the likelihood of an event occurring given that another event has already occurred. It is represented by P(A|B), where P(A) is the probability of event A and P(B) is the probability of event B.

2. How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of two events occurring together (P(A∩B)) by the probability of the first event occurring (P(B)). Mathematically, it can be written as P(A|B) = P(A∩B)/P(B).

3. Can conditional probability be used to predict future events?

Yes, conditional probability can be used to predict future events based on past data. By analyzing the relationship between two events, we can calculate the probability of one event occurring given that the other event has already occurred.

4. What is the difference between conditional probability and joint probability?

Conditional probability measures the likelihood of an event occurring given that another event has already occurred, while joint probability measures the likelihood of two events occurring together. Conditional probability is calculated using the joint probability and the probability of the first event occurring.

5. What are some real-world applications of conditional probability?

Conditional probability has various applications in different fields such as medicine, finance, and sports. For example, it can be used to predict the effectiveness of a certain medication on a patient based on their medical history, or to calculate the probability of a stock increasing in value based on the performance of the market. In sports, it can be used to predict the outcome of a game based on the teams' previous performances.

Back
Top