Gamma Distribution
In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions. It is a generalization of the exponential distribution, with an additional shape parameter that allows for more flexibility in modeling. The gamma distribution is often used in the field of data science to model waiting times, such as the time between arrivals in a queue or the time until a failure in a system. It is also commonly used in finance to model the distribution of asset returns. The gamma distribution has a number of useful properties, including the fact that it is closed under convolution, which makes it useful for modeling sums of random variables. Additionally, it is a conjugate prior for the precision parameter of the normal distribution, which makes it useful in Bayesian statistics.
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