Monte Carlo integration is a computational method used to estimate complex mathematical equations. It involves generating random samples and computing their values, which are then averaged to obtain an approximation of the original function. Synonyms for Monte Carlo integration include stochastic integration, statistical integration, and random sampling. This method is particularly useful for problems that are analytically intractable, such as high-dimensional integrals or those with complex boundaries. Other related techniques include Gaussian quadrature, which involves approximating the integral using weighted sums of function values at predetermined points; and numerical integration, which involves approximating the integral using a numerical algorithm.