onte arlo ethods many algorithms in machine learning and data science make use of monte carlo techniques this gives an introduction to the three main uses of monte carlo simulationto ( simulate random objects and processes in order to observe their behavior( estimate numerical quantities by repeated samplingand ( solve complicated optimization problems through randomized algorithms introduction briefly putmonte carlo simulation is the generation of random data by means of computer these data could arise from simple modelssuch as those described in or from very complicated models describing real-life systemssuch as the positions of vehicles on complex road networkor the evolution of security prices in the stock market in many casesmonte carlo simulation simply involves random sampling from certain probability distributions the idea is to repeat the random experiment that is described by the model many times to obtain large quantity of data that can be used to answer questions about the model the three main uses of monte carlo simulation aresampling here the objective is to gather information about random object by observing many realizations of it for instancethis could be random process that mimics the behavior of some real-life system such as production line or telecommunications network another usage is found in bayesian statisticswhere markov chains are often used to sample from posterior distribution estimation in this case the emphasis is on estimating certain numerical quantities related to simulation model an example is the evaluation of multidimensional integrals via monte carlo techniques this is achieved by writing the integral as the expectation of random variablewhich is then approximated by the sample mean appealing to the law of large numbers guarantees that this approximation will eventually converge when the sample size becomes large optimization monte carlo simulation is powerful tool for the optimization of complicated objective functions in many applications these functions are deterministic and monte carlo simulation