The basic idea behind Monte Carlo simulations is to use random sampling to estimate the behaviour of a system or process when analytical solutions are difficult or impossible to obtain.
These simulations are named after the famous gambling city Monte Carlo in Monaco, as the methods involve randomness and probability, similar to games of chance.
Monte Carlo simulation is widely used in various fields such as engineering, finance, computer science, statistics etc. Some common uses are:
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A Monte Carlo simulation is used to model the probability of different outcomes in a process which cannot easily be expected due to the involvement of random variables. Some of the topics that are involved in Monte Carlo Simulations are:
Correction Step (Measurement Update):
Kk = Pk/k-1.HT.(H.Pk/k-1.HT.R)-1
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Markov chain Monte Carlo methods: This method is commonly known as the MCMC method. It is a powerful method that is used to sample complex and high dimensional probability distribution.
The density of samples generated by MCMC
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