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First, PRIORS. In Bayesian Statistics, we use probability distributions (like a normal, Cauchy, beta...) to represent uncertainty about the value of parameters.
Instead of choosing ONE number for a param, a distribution describes how likely a range of values are
Instead of choosing ONE number for a param, a distribution describes how likely a range of values are
Bayesian Stats works by taking previously known, PRIOR information (this can be from prior data, domain expertise, regularization...) about the parameter
and combining it with data to make the POSTERIOR (the distribution of parameter values AFTER including data)
and combining it with data to make the POSTERIOR (the distribution of parameter values AFTER including data)
