Probability - Condition/Marginal density and Expectation

In summary, the conversation discusses the joint probability density function of two continuous random variables, X and Y, and various calculations involving this function. These calculations include determining probabilities and marginal density functions, calculating expected values, and finding conditional density functions. The conversation also mentions a potential mistake in the setup of one of the calculations and asks for suggestions on how to proceed with another calculation.
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Homework Statement



Let X and Y be contnious random variables with joint probability density function -

[tex]f(x,y) = 10x^2y[/tex] if 0<x<y<1 0 othewise

a) Determine [tex]P( Y < \frac{X}{2})[/tex]

b) Determine [tex]P(x \leq 1/2 | Y < X^2)[/tex]

c) Determine the marginal density functions of X and Y, respectively

d) Determine [tex]E[XY^2][/tex]

e) Determine [tex]E[Y|X = x][/tex]

g) Obtaine the probability density function of E[Y|X]

Homework Equations





The Attempt at a Solution



Did I set up the a - f correctly?

a)

[tex]\int^1_0\int^{X/2}_0 10x^2y dy dx[/tex]

b) [tex]P(A|B) = \frac{P(A \cap B)}{P(B)} \rightarrow \frac{P(X \leq 1/2 \cap Y < X^2)}{P(Y < X^2)}[/tex]

c)

[tex]F_Y (y) = \int^1_y 10x^2y dx[/tex] [tex]F_X (x) = \int^x_0 10x^2y dy[/tex]

d)
[tex]E[XY^2] = \int^1_0\int^x_0 xy^2 10x^2y dy dx[/tex]

e)

[tex]F_{Y|X} (Y|X) = \frac{f(x,y)}{F_X (x)}[/tex]

f)

[tex]E[Y|X] = \int^y_0 y F_{Y|X} (Y|X) dy [/tex]

g) Not sure how to do this one.
 
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  • #2
Any suggestions?

Got a type

e) Determine conditional density function of Y given X = x.

f) Detetmine E[Y|X]
 

1. What is the difference between condition and marginal density in probability?

Condition density refers to the probability of an event occurring under a specific condition or set of conditions. This is denoted as P(A|B), where A is the event and B is the condition. On the other hand, marginal density refers to the probability of an event occurring without any specific conditions. This is denoted as P(A) and is calculated by summing the probabilities of all possible conditions.

2. How do you calculate the expectation of a random variable?

The expectation of a random variable is calculated by multiplying each possible value of the variable by its corresponding probability, and then summing all of these products together. This formula can be written as E(X) = Σx * P(X=x), where X is the random variable, x is a possible value, and P(X=x) is the probability of X taking on that value.

3. What is the law of total probability?

The law of total probability states that for any event A, the probability of A occurring is equal to the sum of the probabilities of A occurring under all possible conditions. Mathematically, this can be expressed as P(A) = ΣP(A|B) * P(B), where A is the event and B is the condition.

4. How is the concept of conditional probability used in real life?

Conditional probability is used in many real-life scenarios, such as predicting the outcome of a medical test, estimating the likelihood of a certain stock price based on market conditions, or determining the probability of a sports team winning based on their past performance and current conditions. It is also used in decision-making processes, such as risk assessment and strategic planning.

5. Can probability be used to accurately predict the future?

No, probability cannot be used to accurately predict the future. It is a tool for quantifying uncertainty and assessing the likelihood of different outcomes, but it cannot determine the exact outcome of a future event. Probability is based on assumptions and data, which may not always be accurate or complete, and it cannot account for unforeseen events or chance occurrences.

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