what is Monte Carlo technique.

what is Monte Carlo technique.

Monte-Carlo Techniques are basically computational techniques...in which random input is generated for ...say a software program to simulate naturally occuring random processes like random thermodynamic motion of particles...
Hope this helps...

Not all simulations are Monte Carlo or pure Monte Carlo simulations. Monte Carlo
implies that at least one random process is emulated and pure Monte Carlo implies all the input processes are emulated. In many simulation applications it is not necessary to simulate all the random processes present in the system.
Since the noise is Gaussian and additive at the input, its net effect at the output of the filter and hence on the decision metric will also be additive and Gaussian. In the absence of distortion, the effect of additive Gaussian noise can be handled analytically without simulation. Even with distortion, the effects of AWGN can
still be characterized analytically for a given value of distortion. However, the distribution of the distortion values introduced by the nonlinearity and filters may be difficult to characterize analytically, but easy to simulate. In such a case, we only need to simulate the cumulative effect of all the functional blocks on the input binary waveform and there is no need to explicitly simulate the noise waveform.
MC simulations in which only some (but not all) input processes into the system are
simulated explicitly while the effects of other processes are handled using analytical techniques are called partial MC or quasianalytical (QA) simulations. The main advantage of a QA simulation is that it will require the simulation of fewer samples than a pure MC simulation to produce estimates with the same accuracy.

hi

Monte Carlo simulation is a stochastic technique used to solve mathematical problems. The word "stochastic" means that it uses random numbers and probability statistics to obtain an answer. Monte Carlo methods were originally developed for the Manhattan Project during World War II. However, they are now applied to a wide range of problems - nuclear reactor design, econometrics, stellar evolution, stock market forecasting etc.

Similarly, Monte Carlo methods randomly select values to create scenarios of a problem. These values are taken from within a fixed range and selected to fit a probability distribution [e.g. bell curve, linear distribution, etc.]. This is like rolling a dice. The outcome is always within the range of 1 to 6 and it follows a linear distribution - there is an equal opportunity for any number to be the outcome.

In Monte Carlo simulation, the random selection process is repeated many times to create multiple scenarios. Each time a value is randomly selected, it forms one possible scenario and solution to the problem. Together, these scenarios give a range of possible solutions, some of which are more probable and some less probable.

When repeated for many scenarios [10,000 or more], the average solution will give an approximate answer to the problem. Accuracy of this answer can be improved by simulating more scenarios. In fact, the accuracy of a Monte Carlo simulation is proportional to the square root of the number of scenarios used.


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