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Monte Carlo methods are used mostly for problems with a large number of variables and dimensions. For example, [[Bayesian statistics]] often involve calculating dozens of variables each of which can exist along a continuation of hundreds of thousands or millions of values. In order to find the best solution each value of each variable would have to be integrated. This is an intractable problem even for advanced computers to solve. The development of Monte Carlo methods has allowed Bayesian solutions to large scale problems to be calculated.  

Monte Carlo methods are used mostly for problems with a large number of variables and dimensions. For example, [[Bayesian statistics]] often involve calculating dozens of variables each of which can exist along a continuation of hundreds of thousands or millions of values. In order to find the best solution each value of each variable would have to be integrated. This is an intractable problem even for advanced computers to solve. The development of Monte Carlo methods has allowed Bayesian solutions to large scale problems to be calculated.  

==See Also==

==See also==

[[Genetic algorithm]]

[[Genetic algorithm]]

==References==

==References==

* Bernd A. Berg, ''Markov Chain Monte Carlo Simulations and Their Statistical Analysis (With Web-Based Fortran Code)'', World Scientific 2004, ISBN 981-238-935-0.

* Bernd A. Berg, ''Markov Chain Monte Carlo Simulations and Their Statistical Analysis (With Web-Based Fortran Code)'', World Scientific 2004, ISBN 981-238-935-0.

[[category:Probability and Statistics]]

[[Category:Probability and Statistics]]