Markov Chain Monte Carlo — or How to Estimate Unknown Probability Function

In the field of probability and statistics, it is not uncommon to encounter situations where the probability distribution function cannot be calculated directly. One example of such a situation is the Bayesian inference. A popular example of Bayesian inference is the estimation of model parameters. The parameters are the elements that define the model structure. For example, for a normal distribution, the parameters are the mean and the variance. For an exponential distribution, the parameter is λ. In this case, we usually have some samples x that come from a distribution with unknown parameters θ, but we assume we have some prior knowledge about θ we can express using a probability function p(θ). Using Bayes theorem, we can find the probability for θ according to observed data x. <a href="https://python.plainenglish.io/markov-chain-monte-carlo-or-how-to-estimate-unknown-probability-function-a2308bf0ad59">Click Here</a>