Combination of Probability Density Functions

In summary, to calculate the probability density function of a function Y of two variables A and B with known individual probability density functions, you can combine the PDF's by first scaling the density function of B and then convolving it with the density function of A. This will result in the density function for Y.
  • #1
FrankDrebon
9
0
Hi all,

I need to calculate the probability density function [itex]f\left( Y \right)[/itex] of a function [itex]Y[/itex] of two variables [itex]A[/itex] and [itex]B[/itex] with known individual probability density functions [itex]f\left( A \right)[/itex] and [itex]f\left( B \right)[/itex]. What is the correct way to combine the PDF's?

Specifically, I have a variable Y dependent on A and B by:

[itex]Y = A + 2B[/itex]

I know [itex]f\left( A \right)[/itex] and [itex]f\left( B \right)[/itex], how do I write [itex]f\left( Y \right)[/itex] in terms of [itex]f\left( A \right)[/itex] and [itex]f\left( B \right)[/itex]?

Stats isn't my strong point so apologies if this is trivial!

F
 
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  • #2
The most direct way is to do it in two steps.
Step 1: C = 2B (scale density function).
Step 2: Y = A + C (convolution of density functions of A and C results in density function for Y).
 

1. What is a combination of probability density functions (PDFs)?

A combination of probability density functions is a mathematical concept used to describe the probability distribution of a random variable that is determined by combining two or more individual PDFs. It is used to represent the likelihood of different outcomes occurring in a given event.

2. How is a combination of PDFs calculated?

A combination of PDFs is calculated by taking the weighted average of the individual PDFs. This means that each PDF is multiplied by a weight, which represents the probability of that PDF occurring, and then the resulting values are summed together to create the combined PDF.

3. What is the difference between a combination of PDFs and a convolution of PDFs?

A combination of PDFs and a convolution of PDFs are similar concepts, but there are some key differences. A combination of PDFs is a weighted average of the individual PDFs, while a convolution of PDFs is a mathematical operation that combines two or more PDFs to create a new PDF. Additionally, a combination of PDFs typically results in a smoother distribution, while a convolution of PDFs may have more complex shapes.

4. In what situations is a combination of PDFs commonly used?

A combination of PDFs is commonly used in situations where there are multiple sources of uncertainty or randomness, and the overall probability distribution is determined by the combination of these sources. This can include areas such as finance, economics, and engineering, where multiple factors can influence the outcome of an event.

5. Can a combination of PDFs be used to model non-linear relationships?

Yes, a combination of PDFs can be used to model non-linear relationships between variables. This is because the weights assigned to each individual PDF can account for the non-linear effects, and the resulting combination will reflect the overall probability distribution of the non-linear relationship.

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