Integration involving Gamma Distribution

In summary, we used the definition of the Gamma function to simplify the integration and arrived at the final solution of \frac{\Gamma(\alpha + x)}{B \alpha!}.
  • #1
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Homework Statement


Evaluate [tex]
\int^{\infty}_0 \frac{1}{B^{\alpha} \Gamma(\alpha)} \theta^{\alpha + x -1}e^{- \theta(1+B)/B} d \theta
[/tex]


Homework Equations





The Attempt at a Solution



[tex]
\int^{\infty}_0 \frac{1}{B^{\alpha} \Gamma(\alpha)} \theta^{\alpha + x -1}e^{- \theta(1+B)/B}d \theta
[/tex]

[tex]
= \frac{1}{B^{\alpha} \Gamma(\alpha)} \int^{\infty}_0 \theta^{\alpha + x -1}e^{- \theta(1+B)/B}d \theta
[/tex]

Let [tex]u = \theta(1+B)/B[/tex] and [tex]du = \frac{1 + B}{B} d\theta[/tex]

[tex]
= \frac{1}{B^{\alpha} \Gamma(\alpha)} \int^{\infty}_0 \frac{1 + B}{B} \frac{Bu}{1+B}^{\alpha + x -1}e^{- u} du
[/tex]

I'm stuck here. Not sure what to do next.
 
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  • #2
Can you please provide some guidance?

Hi there, it looks like you're on the right track with your integration. Let's continue from where you left off:

= \frac{1}{B^{\alpha} \Gamma(\alpha)} \int^{\infty}_0 \frac{1}{B} u^{\alpha + x -1}e^{- u} du


= \frac{1}{B^{\alpha + 1} \Gamma(\alpha)} \int^{\infty}_0 u^{\alpha + x -1}e^{- u} du


Now, we can use the definition of the Gamma function to simplify the expression:

= \frac{\Gamma(\alpha + x)}{B^{\alpha + 1} \Gamma(\alpha)}


= \frac{\Gamma(\alpha + x)}{B \Gamma(\alpha + 1)}


= \frac{\Gamma(\alpha + x)}{B \alpha!}


Therefore, our final solution is:

\int^{\infty}_0 \frac{1}{B^{\alpha} \Gamma(\alpha)} \theta^{\alpha + x -1}e^{- \theta(1+B)/B} d \theta = \frac{\Gamma(\alpha + x)}{B \alpha!}

I hope this helps! Let me know if you have any further questions.
 

1. What is the gamma distribution?

The gamma distribution is a continuous probability distribution that is commonly used to model data that is skewed to the right, such as waiting times or income levels. It is defined by two parameters, shape and scale, and has a wide range of applications in statistics, economics, and engineering.

2. How is the gamma distribution integrated?

The gamma distribution can be integrated using the gamma function, which is a generalization of the factorial function for non-integer values. The integral of the gamma distribution is used to calculate probabilities and expected values for different values of the parameters.

3. What is the relationship between the gamma distribution and the exponential distribution?

The exponential distribution is a special case of the gamma distribution, where the shape parameter is equal to 1. This means that the exponential distribution is a special case of the gamma distribution where the rate parameter is equal to the scale parameter. Both distributions are commonly used to model waiting times, but the gamma distribution allows for a wider range of shapes and can better fit skewed data.

4. Can the gamma distribution be used for both continuous and discrete data?

The gamma distribution is only used for continuous data, as it is a continuous probability distribution. However, it can be used to model discrete data by approximating it with a continuous distribution. This is commonly done by rounding the data or using a smaller interval width.

5. How is the gamma distribution used in hypothesis testing?

The gamma distribution is used in hypothesis testing to calculate p-values, which are used to determine the statistical significance of a result. The p-value is calculated by integrating the gamma distribution and comparing it to a critical value. If the p-value is less than the critical value, the result is considered statistically significant.

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