Markov Chain Monte Carlo: Made Simple Once and For All

A Monte Carlo method or simulation is a type of computational algorithm that consists of using sampling numbers repeatedly to obtain numerical results in the form of the likelihood of a range of results of occurring. In other words, a Monte Carlo simulation is used to estimate or approximate the possible outcomes or distribution of an uncertain event. A simple example to illustrate this is by rolling two dice and adding their values. We could easily compute the probability of each outcome but we could also use Monte Carlo methods to simulate 5,000 dice-rollings (or more) and get the underlying distribution. <a href="https://towardsdatascience.com/markov-chain-monte-carlo-made-simple-once-and-for-all-e86e8384186c">Read More</a>