Exponential Family

Exponential family is a class of probability distributions that have a specific mathematical form. In the context of data science, exponential family distributions are commonly used to model data with a limited number of parameters. The exponential family includes many well-known distributions such as the normal, Poisson, and binomial distributions. The key feature of the exponential family is that the probability density function can be written as a product of two terms: a function of the observed data and a function of the parameters. This allows for efficient computation of likelihoods and posterior distributions. Exponential family distributions are widely used in machine learning algorithms such as generalized linear models, Bayesian networks, and hidden Markov models.
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