Conditional distribution for random variable on interval

In summary, the goal is to find the conditional distribution function and density for a random variable X in the interval I = (a,b), given that P(X in I) > 0. The equation for the conditional density is P(X < x + Δx | a ≤ X ≤ b) = (P(X < x + Δx and a ≤ X ≤ b)) / (P(a ≤ X ≤ b)). The numerator can be expressed as the product of the probability density function and the interval. The denominator is the integral of the probability density function over the interval.
  • #1
Srumix
36
0

Homework Statement



Find the conditional distribution function and density for the random variable X defined on R given that X is in some interval I = (a,b) where P(X in I) > 0. Assume that the density and distribution for the random variable X is known

Homework Equations



fX|X[itex]\in[/itex]I = P(X[itex]\leq[/itex]x | X[itex]\in[/itex] I) = fX,X[itex]\in[/itex]I(x,x)/fX[itex]\in[/itex]I(x)

The Attempt at a Solution



I'm sorry, but my latex skills are very poor so I will try to describe in words what my problem is.
The problem I'm having is that I know how to calculate the probability of P(X in I) since we just take the integral of the density function over the interval I in question. However, what do I do with the "joint" distribution fX,X[itex]\in[/itex]I(x,x) that I need for the definition of conditional distribution? That is what I can't figure out.

Thanks in advance!
 
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  • #2
Srumix said:

Homework Statement



Find the conditional distribution function and density for the random variable X defined on R given that X is in some interval I = (a,b) where P(X in I) > 0. Assume that the density and distribution for the random variable X is known

Homework Equations



fX|X[itex]\in[/itex]I = P(X[itex]\leq[/itex]x | X[itex]\in[/itex] I) = fX,X[itex]\in[/itex]I(x,x)/fX[itex]\in[/itex]I(x)

The Attempt at a Solution



I'm sorry, but my latex skills are very poor so I will try to describe in words what my problem is.
The problem I'm having is that I know how to calculate the probability of P(X in I) since we just take the integral of the density function over the interval I in question. However, what do I do with the "joint" distribution fX,X[itex]\in[/itex]I(x,x) that I need for the definition of conditional distribution? That is what I can't figure out.

Thanks in advance!

The conditional density is the coefficient of ##\Delta x## in the first-order (small-##\Delta x##) expansion of
[tex]\text{P} \{ x < X < x + \Delta x | a \leq X \leq b \}
= \frac{\text{P} \{ x < X < x+ \Delta x \: \& \: a \leq X \leq b \}}{\text{P} \{ a \leq X \leq b \}} [/tex]
For ##x \in (a,b)##, can you figure out what is the numerator, in terms of the probability density function ##f(.)##? Can you figure out the denominator?
 

Related to Conditional distribution for random variable on interval

1. What is a conditional distribution for a random variable on an interval?

A conditional distribution is a probability distribution that shows the likelihood of a random variable taking on certain values within a specific interval, given that another random variable has already taken on a certain value. It is typically denoted as P(X | Y), where X is the random variable of interest and Y is the given condition.

2. How is a conditional distribution calculated?

A conditional distribution is calculated by dividing the joint probability of the two random variables (P(X, Y)) by the marginal probability of the given condition (P(Y)). This can be written as P(X | Y) = P(X, Y) / P(Y).

3. What is the difference between a marginal distribution and a conditional distribution?

A marginal distribution shows the probability distribution of a single random variable without taking into account any other variables. A conditional distribution, on the other hand, shows the probability distribution of a random variable within a specific interval, given that another variable has already taken on a certain value.

4. How is a conditional distribution for a continuous random variable represented?

A conditional distribution for a continuous random variable is typically represented using a conditional probability density function (PDF). This function shows the likelihood of the random variable falling within a specific interval, given a certain condition.

5. In what situations is understanding conditional distributions useful?

Conditional distributions are useful in situations where there is a relationship between multiple variables and the likelihood of certain events occurring. They can help to uncover patterns and dependencies between variables, and can also be used in predictive modeling and decision-making.

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