Finding the Expected Value of X with a Probability Density Function

In summary, the conversation discusses finding the value of & in terms of b, using the given identities to show the expected value of X, and using the Rayleigh distribution probability density function to solve a problem. The solution involves separating the integral into two parts and using substitutions to eventually arrive at the correct answer. The importance of providing complete problem statements is also mentioned.
  • #1
clockworks
5
0

Homework Statement



f(x)=&(x-a)exp((-(x-a)^2)/b) where a and b are constants

Homework Equations



find & in terms of b:

show that the expected value of X is given by
X=a + sqrt(pi*b/4)
identity given
x(x-a)=(x-a)^2+a(x-a)
and integral from 0 to infinity of x^2*exp-x^2 dx=sqrt (pi) /4

The Attempt at a Solution



i found &=2/b and thought my solution was coherent but seeing as i can't answer the next question I am confused as to where i went wrong .
i manage to find X= a + sqrt(pi/4) but can't get that b into the square root no matter what i try .
i separated into 2 integrals using the first identity then set Y=(x-a)/sqrt b and used the second identity to get sqrt (pi /4)( the other integral giving the expected a)
 
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  • #2
Did you remember to write dx in terms of dy when you did the substitution?

By the way, it would help in the future if you provide the complete problem statement. You didn't tell us what the domain of f(x) was, for instance.
 
  • #3
im sorry the domain of fx is the function provided for x>=a and 0 otherwise
 
  • #4
also the probability density function is a Rayleigh distribution
 
  • #5
using wikipedia i found the correct answer (using the formulas that use the variance and such) but id still like to know how to recalculate it using the identities given so my question still stands :D
 
  • #6
thx vela problem solved :D
 

FAQ: Finding the Expected Value of X with a Probability Density Function

What is a probability density function (PDF)?

A probability density function (PDF) is a mathematical function that describes the probability of a continuous random variable falling within a certain range of values. It is used to visualize and analyze the probability distribution of a continuous variable.

How is a PDF different from a probability distribution function (PDF)?

A PDF is the graph or chart that represents the probability distribution of a continuous random variable, while a probability distribution function (PDF) is the mathematical equation that defines the shape of the PDF. In other words, the PDF is a visual representation of the PDF.

What are the properties of a PDF?

There are several key properties of a PDF, including:
1. The area under the curve of the PDF must equal 1.
2. The PDF can never be negative.
3. The PDF must approach 0 as the variable approaches negative or positive infinity.
4. The PDF can be used to determine the probability of a continuous random variable falling within a certain range of values by finding the area under the curve within that range.

How is a PDF used in probability and statistics?

A PDF is used in probability and statistics to analyze and understand the probability distribution of a continuous random variable. It can be used to calculate the likelihood of a certain outcome, determine the mean and variance of a variable, and compare different distributions.

What are some common types of PDFs?

Some common types of PDFs include the normal distribution, uniform distribution, exponential distribution, and beta distribution. Each of these distributions has its own unique shape and properties, making them useful for different types of data analysis and modeling.

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