It took me a long time to understand Bayesian statistics. So many angles from which to approach it: the Bayes' Theorem, probability as a degree of belief, Bayesian updating, priors, and posteriors, ... But my favorite angle is the following first principle:

Thanks for this article! I'm taking a bayesian statistics course and would to clarify something. Is it true that P(X) is the normalizing constant, and in practice, we can just take the numerator P(X|θ) P(θ) which is proportional to the posterior distribution, and MCMC is used to sample from an unknown form of the posterior distribution. And there is no need to deal with P(X)?

I don't think I've laughed this much reading an article on Bayesian modeling. This was such a fun read!

edited May 9Thanks for this article! I'm taking a bayesian statistics course and would to clarify something. Is it true that P(X) is the normalizing constant, and in practice, we can just take the numerator P(X|θ) P(θ) which is proportional to the posterior distribution, and MCMC is used to sample from an unknown form of the posterior distribution. And there is no need to deal with P(X)?