The answer really depends on what your simulation is about. The answer would be different for an MCMC simulation for Bayesian inference and simulating waiting lines at a bank. Best, David

Dear Ahmad;

It all depend on what you are simulation or what is your future plans about MC simulation. Monte Carlo is a only method and you can find different kind of Simulation codes since each of them has different abilities and input writing style. If you extend your question or studies little more, i think i can help you much more significant. Regards

I think inversion sampling will be best provided you could able integrate the pdf and then take inverse.  Otherwise you have to go for rejection technique of sampling. There are other sampling methods but it is important what you have to simulate.

Dear Ahmad,

if to speak about pseudorandom number generators for computer-based simulation using the Monte Carlo method, in my research https://www.researchgate.net/publication/314232345_MEX_function_for_multivariate_analysis_of_reliability_indices_depending_on_maintenance_periodicity_of_radio_communication_equipment, I apply a generator based on  L'Ecuyer algorithm with a long period of about 10^8. Then the Inverse transform sampling is used in order to convert random numbers from a uniform distribution into a required probability distribution. 

Kind regards,

Alexander

Code MEX function for multivariate analysis of reliability indice...

How to reduce computational burden of Monte Carlo simulations?

Dear Umair,

probably, you could apply some of the methods for variance reduction ( https://en.wikipedia.org/wiki/Variance_reduction ), then, you will need to performe less runs of a simulation model. For example, you could try stratified sampling technique