Calculating Posterior distrib of gamma funct

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In summary, the conversation is about calculating the posterior distribution of the gamma function, with one person asking for help and another suggesting using LaTex for equations. The equation in question is similar to the one given on Wikipedia for the Gamma distribution, and the person is looking to convert it into a posterior distribution using a specific formula also found on Wikipedia.
  • #1
duraipandian
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guys
can u please help me out in calculating the posterior distib of the gamma function of form :

p(lambda/alpha,beta) = (beta^alpha)*lambda^(alpha-1)*(e^(-beta*lamda)))/gamma(a)

lambda>0 mean=alpha/beta mode = (alpha-1)/beta ;alpha>0

the integration seems really tuff which makes me feel tht I am going in the wrong direction.. can u guys help me out with this please...
 
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  • #2
I have no idea what you mean by the distribution of the Gamma function. The Gamma function has a Dirac distribution (same as saying it is deterministic). If you mean the Gamma distribution (you equation kinda looks like it), you may want to read http://en.wikipedia.org/wiki/Gamma_distribution
I would kindly ask that you use LaTex when writing equations or using simple constant/variables like a,b,c,d,e... I can't decipher the equation.
 
  • #3

1. What is the purpose of calculating the posterior distribution of gamma function?

The posterior distribution of gamma function is used to estimate the parameters of a gamma distribution based on observed data. It allows us to make more accurate and reliable predictions about future outcomes.

2. How is the posterior distribution of gamma function calculated?

The posterior distribution of gamma function is calculated using Bayesian inference, which involves combining prior knowledge or beliefs about the parameters with new data to obtain a more accurate estimate.

3. What are the assumptions involved in calculating the posterior distribution of gamma function?

The assumptions involved in calculating the posterior distribution of gamma function include the assumption that the data follows a gamma distribution, and that the prior distribution is known or can be estimated.

4. How is the posterior distribution of gamma function different from the prior distribution?

The prior distribution represents our beliefs about the parameters before observing any data, while the posterior distribution takes into account the observed data and updates our beliefs accordingly. Therefore, the posterior distribution is generally more accurate and informative than the prior distribution.

5. Can the posterior distribution of gamma function be used for any type of data?

The posterior distribution of gamma function is applicable to any type of data that follows a gamma distribution. However, if the data does not follow a gamma distribution, a different type of posterior distribution, such as a normal distribution, may be more appropriate.

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