Evaluating Conditional Probability of Several Random Variables

In summary, the homework statement asks for the probability that two iids, X_1 and X_2, differ by less than two standard deviations.
  • #1
rayge
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Homework Statement


Let [itex]X_1, X_2, X_3[/itex] be iid with common pdf [itex]f(x)=exp(-x)[/itex], [itex]0<x<infinity[/itex], [itex]0[/itex] elsewhere.

Evaluate [itex]P(X_1<X_2 | X_1<2X_2)[/itex]

Homework Equations


[itex]f(X|Y) = f(x,y)/f(y)[/itex]

The Attempt at a Solution


Since [itex]P(X_1<X_2)[/itex] is a subset of [itex]P(X_1<2X_2)[/itex], the intersection (edited, at first said union) should be [itex]P(X_1<X_2)[/itex], so the conditional should be [itex]P(X_1<X_2)/P(X_1<2X_2)[/itex] (edited). I evaluate this and get [itex](exp(-x_2) - 1 )/ (exp(-2x_2 ) - 1)[/itex], so I'm going wrong somewhere. Any suggestions welcome!
 
Last edited:
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  • #2
rayge said:

Homework Statement


Let [itex]X_1, X_2, X_3[/itex] be iid with common pdf [itex]f(x)=exp(-x)[/itex], [itex]0<x<infinity[/itex], [itex]0[/itex] elsewhere.

Evaluate [itex]P(X_1<X_2 | X_1<2X_2)[/itex]


Homework Equations


[itex]f(X|Y) = f(x,y)/f(y)[/itex]

The Attempt at a Solution


Since [itex]P(X_1<X_2)[/itex] is a subset of [itex]P(X_1<2X_2)[/itex], the union should be [itex]P(X_1<X_2)[/itex], so the conditional should be [itex]P(X_1<2X_2)/P(X_1<2X_2)[/itex]. I evaluate this and get [itex](exp(-x_2) - 1 )/ (exp(-2x_2 ) - 1)[/itex], so I'm going wrong somewhere. Any suggestions welcome!

What value has ##x_2?## There is no ##x_2## mentioned in the question---only the random variable ##X_2##. Also, you should look at an intersection, not a union: the intersection is ##\{X_1 < X_2\}## while the union is ##\{X_1 < 2X_2\}##. Furthermore, you say one thing and compute something else.

Also, what role does ##X_3## have in the question?
 
  • #3
Edited, you are right. I had evaluated [itex]P(X_1<X_2)/P(X_1<2X_2)[/itex] to get the answer.

[itex]X_3[/itex] is the second part of the problem, not used in this part. Sorry for the confusion.
 
  • #4
rayge said:
Edited, you are right. I had evaluated [itex]P(X_1<X_2)/P(X_1<2X_2)[/itex] to get the answer.

[itex]X_3[/itex] is the second part of the problem, not used in this part. Sorry for the confusion.

No, that is not what you calculated. You calculated
[tex] \frac{P(X_1 < x_2)}{P(X_1 < 2x_2)}[/tex]
for some un-specified value of the number ##x_2##.

How do you calculate ##P(X_1 < X_2)##? Here, I wrote and I mean ##X_2##---the random variable--not a number ##x_2##. Same question for ##P(X_1 < 2X_2)##.
 
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  • #5
My guess is that it's the probability that [itex]X_1[/itex] is less than the mean of [itex]X_2[/itex], not some [itex]x_2[/itex].
 
  • #6
rayge said:
My guess is that it's the probability that [itex]X_1[/itex] is less than the mean of [itex]X_2[/itex], not some [itex]x_2[/itex].

Please: no guesses. Go back to your probability textbook and read the appropriate sections, or consult your course notes. This is all really basic material about bivariate distributions, etc.
 

1. What is conditional probability?

Conditional probability is the likelihood of an event occurring given that another event has already occurred. It is calculated by dividing the probability of the two events occurring together by the probability of the first event occurring alone.

2. How is conditional probability calculated?

Conditional probability is calculated using the formula P(A|B) = P(A∩B)/P(B), where P(A|B) represents the conditional probability of event A given event B, P(A∩B) represents the probability of both A and B occurring, and P(B) represents the probability of event B occurring.

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

Conditional probability and joint probability both involve the likelihood of two events occurring together. However, conditional probability is calculated with the assumption that the first event has already occurred, while joint probability is calculated without any prior knowledge of the events.

4. How do you evaluate conditional probability for several random variables?

Evaluating conditional probability for several random variables involves breaking down the problem into smaller conditional probabilities and then using the chain rule to calculate the overall probability. This involves multiplying the individual probabilities for each event in the chain.

5. What are some applications of evaluating conditional probability of several random variables?

Evaluating conditional probability of several random variables has many real-world applications, including risk assessment in insurance, predicting stock market trends, and analyzing results in medical studies. It is also used in fields such as machine learning, genetics, and finance to make informed decisions based on multiple variables.

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