Bayesian Statistics
Generalized linear models (GLMs) are a class of statistical models that extend traditional linear regression by allowing the response variable to have a distribution other than a normal distribution. GLMs connect the mean of the response variable to a linear predictor through a link function, accommodating various types of data such as binary, count, or proportion data. They are particularly valuable in Bayesian analysis and probabilistic programming, allowing for flexible modeling in various statistical software like Stan and R.
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