Question about Beta distribution (probability)

In summary, the Beta distribution is a positive continuous random variable defined on [0,1] with two parameters, usually denoted as alpha and beta. Its density function involves the Beta function, and it is used to model the probability of success in Bernoulli trials. In order for the density function to be a legitimate PDF, the value of 'c' must be such that the integral over its domain equals 1. This can be found using the integral formula of the Beta function. The expected values of X and X^2 can also be found using the facts about the Beta and Gamma functions.
  • #1
iiiiaann
22
0

Homework Statement



1. The Beta distribution: A Beta random variable is a positive continuous random variable
de ned on [0; 1] that has two parameters associated with it, usually denoted and . Both
and must be positive real numbers. The beta distribution is used to model the probability
of success in Bernoulli trials when each trial has a random success probability - e.g. tossing
randomly selected coins.
The density function of a beta random variable X with parameters [itex]\alpha[/itex] and [itex]\beta[/itex] is
Image_2011.10.21_2_57_51_AM.png

for 0 < x < 1.
The Beta distribution gets its name from the fact that its density function involves the so-called
Beta function. Here are several facts about the Beta function B(s; t):
Image_2011.10.21_2_59_00_AM.png

(a) Let X be Beta with arbitrary parameters and . Show how to use the facts above
about the Beta function to derive c.
(b) Use the fact above about the Beta function and the facts from Homework 6 about the
Gamma function to fi nd E(X) and E(X2)
) without actually doing any integration yourself.
Check your answers against Wikipedia

Homework Equations



don't know of any

The Attempt at a Solution



don't know where to start either of these
 
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  • #2
iiiiaann said:

Homework Statement



1. The Beta distribution: A Beta random variable is a positive continuous random variable
de ned on [0; 1] that has two parameters associated with it, usually denoted and . Both
and must be positive real numbers. The beta distribution is used to model the probability
of success in Bernoulli trials when each trial has a random success probability - e.g. tossing
randomly selected coins.
The density function of a beta random variable X with parameters [itex]\alpha[/itex] and [itex]\beta[/itex] is
Image_2011.10.21_2_57_51_AM.png

for 0 < x < 1.
The Beta distribution gets its name from the fact that its density function involves the so-called
Beta function. Here are several facts about the Beta function B(s; t):
Image_2011.10.21_2_59_00_AM.png

(a) Let X be Beta with arbitrary parameters and . Show how to use the facts above
about the Beta function to derive c.
(b) Use the fact above about the Beta function and the facts from Homework 6 about the
Gamma function to fi nd E(X) and E(X2)
) without actually doing any integration yourself.
Check your answers against Wikipedia

Homework Equations



don't know of any

The Attempt at a Solution



don't know where to start either of these

What must be the value of 'c' in order that f(x) be a legitimate pdf? What is the formula for EX, in terms of the pdf f(x)? How can you express the result in terms of functions that you are permitted to use in this exercise (i.e., Beta and Gamma functions)? Ditto for E(X^2).

RGV
 
  • #3
i know that for f(x) to be a legitimate pdf, the integral from negative infinity to positive infinity of f(x) has to equal 1, but i don't know how that could help me find c in this situation
 
  • #4
But in this case, you don't need to integral it from -infinity to infinity. Only integral it over its domain (or over the interval where f is positive). Once you notice its domain, compare your resulting integral with the integral formula of the beta function. You should get the answer then.
 

1. What is a beta distribution?

A beta distribution is a continuous probability distribution that is used to model and analyze data that has a range of possible values between 0 and 1. It is often used to represent proportions, probabilities, and rates.

2. What are the characteristics of a beta distribution?

A beta distribution is characterized by two parameters, alpha (α) and beta (β), which determine the shape and location of the distribution. Alpha and beta must both be positive values. The distribution is symmetric when alpha = beta, and becomes increasingly skewed as the values of alpha and beta differ.

3. How is a beta distribution different from a normal distribution?

While both distributions are continuous and can take on a wide range of values, a normal distribution is symmetrical and has a bell-shaped curve, while a beta distribution can be asymmetrical and has a curve that is more flattened or elongated. Additionally, the values of a normal distribution can range from negative infinity to positive infinity, while a beta distribution is limited to the range of 0 to 1.

4. What are some common applications of the beta distribution?

The beta distribution is frequently used in fields such as statistics, finance, economics, and engineering to model and analyze data that represents proportions, probabilities, and rates. It is commonly used in quality control, market research, and risk management.

5. How is the beta distribution related to the binomial distribution?

The beta distribution and the binomial distribution are closely related, as the beta distribution is often used as a prior distribution for the parameter p in a binomial distribution. This means that the beta distribution can be thought of as a distribution of probabilities, while the binomial distribution is a distribution of counts or outcomes.

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