Stochastic gradient descent is a staple in machine learning, but Markov chain Monte Carlo (MCMC) stands out in Bayesian networks. The evolution of MCMC, rooted in World War II physics, has led to more efficient algorithms that make Bayesian statistics increasingly accessible. With modern computing power, complex models can now be tackled, allowing for the approximation of posterior distributions that were once nearly impossible to compute by hand.
