Conjugacy According to Bayes’ theorem, the posterior is proportional to the product of the prior and the likelihood. The specification of the prior can be tricky for two reasons: First, the prior should encapsulate our knowledge about the problem before we see any data. This is often difficult to describe. Second, it is often not possible to compute the posterior distribution analytically. However, there are some priors that are computationally convenient and are called conjugate priors. In Bayesian probability theory, if the posterior distribution $p(θ | x)$ is in the same probability distribution family as the prior probability distribution $p(θ)$, the prior and posterior are then called conjugate distributions, and the prior is called a conjugate prior for the likelihood function $p(x | θ)$. A conjugate prior is an algebraic convenience, giving a closed-form expression for the posterior; otherwise numerical integration may be necessary. Further, conjugat...
This blog is written for the following two courses of KTU using python. CST284-Mathematics for Machine Learning-KTU Minor course and CST294-Computational Fundamentals for Machine Learning-KTU honors course. Queries can be send to Dr Binu V P. 9847390760