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This simulation technique is used to describe the chances of different outcomes happening in a process. Monte Carlo simulation is often used for processes whose predictions are inhibited by random variables. It helps analysts understand how uncertainty and risks affect forecasting and prediction models. Monte Carlo simulation is applied for a wide range of problems in various fields including supply chain, finance, engineering, etc. Some experts also refer to this method as multiple probability simulation
Markov chain is a relatively simple technique that is used to model statistical random processes. The probability of each event in the Markov chain depends on the state that the previous event attains. Depending on the sample, the sequence of events varies in length and order. Deterministic statistics cannot be used to describe this chain of events. Markov chains support the modeling of stochastic processes.
Bayesian methods are statistical techniques that are founded on the Bayes’ theorem. In these methods, what we know about the parameters in our statistical model is updated with information we have observed from our data. This approach applies the theory of probability to solve problems in statistics. In other words, inferences made using Bayesian statistics are interpreted as the confidence that the researcher possesses about a particular event occurring.