Support of Continuous Conditional Density Functions (Probability)

In summary, the joint probability density function f(x,y) = x + y for continuous random variables X and Y has a support of {0 < x < 1, 0 < y < 1}, meaning it takes positive values over this region and zero elsewhere. The probability density function for X, g(x) = x + (1/2), is derived by integrating f(x,y) with respect to y over 0 < y < 1. The support of this function is 0 < x < 1. The support of the conditional density function h(y|x) = f(x,y)/g(x) is also {0 < x < 1, 0 < y < 1}, as it is well
  • #1
Legendre
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f(x,y) = x + y is the joint probability density function for continuous random variables X and Y. The support of this function is {0 < x < 1, 0 < y < 1}, which means it takes positive values over this region and zero elsewhere.

g(x) = x + (1/2) is the probability density function of X, derived by integrating f(x,y) with respect to y, over 0 < y < 1. Since we "took y out of the equation", the support of this function should be 0 < x < 1.

Question :

What is the support of the conditional density function h(y|x) = f(x,y)/g(x)?

My guess is that this function is well defined and takes non zero values only over region {0 < x < 1, 0 < y < 1} and so this is the support.

But the book I am using states 0 < y < 1 instead. Is it a typo?
 
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  • #2
I just realized my mistake :

x is fixed since we are conditioning Y on a particular value of X = x. So the 0 < x < 1 is irrelevant. Or rather, the conditional probability density for a given X = x is a function of y only. So only the value of y matters in defining the support.

I feel like an idiot lol.
 

Related to Support of Continuous Conditional Density Functions (Probability)

1. What is a continuous conditional density function?

A continuous conditional density function is a mathematical function that describes the probability of an outcome occurring, given a certain set of conditions. It is used to model continuous random variables, such as time or distance, and can be used to make predictions and calculate probabilities.

2. How is a continuous conditional density function different from a regular probability function?

A continuous conditional density function differs from a regular probability function in that it takes into account a specific set of conditions. This means that the probability of an outcome occurring is dependent on the values of the conditions, rather than being a fixed probability for all cases.

3. What is the purpose of using continuous conditional density functions?

The purpose of using continuous conditional density functions is to model and analyze complex systems that involve continuous random variables. This allows for more accurate predictions and calculations of probabilities, taking into account the effects of different conditions on the outcomes.

4. How is the support of a continuous conditional density function determined?

The support of a continuous conditional density function is determined by the range of values that the random variable can take on. This range is usually defined by the conditions that are being considered and can be visualized using a graph or equation.

5. What are some real-world applications of continuous conditional density functions?

Continuous conditional density functions have many real-world applications, such as in finance, weather forecasting, and machine learning. They can be used to model stock prices, predict weather patterns, and train algorithms to make decisions based on certain conditions.

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