Conjugate priors are a special type of prior distribution in Bayesian statistics that, when combined with a likelihood function, result in a posterior distribution that is in the same family as the prior distribution. This property greatly simplifies the process of updating beliefs based on new data, as it allows for analytical solutions to be derived easily. Conjugate priors play a crucial role in determining the relationships between prior and posterior distributions, facilitating more straightforward calculations and interpretations.
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