This technique can be implemented by moving particles in each direction at a series of time steps by steps uniformly distributed between plus and minus square root quantity twice diffusion coefficient times time step to model diffusion.
This technique can be implemented by moving particles in each direction at a series of time steps by steps uniformly distributed between plus and minus square root quantity twice diffusion coefficient times time step to model diffusion. This technique can be used to estimate integrals, providing a value of volume times sampled average of integrand in the naive form. In this technique, that mean is obtained by integrating a uniform pdf to obtain a cdf, then summing over repeated evaluations of the integrand at points corresponding to the value for the cdf at a (*) random point drawn from the unit interval. This technique can be made to converge faster by using reduction-of-variance techniques like importance sampling. For 10 points, name this class of algorithms that work by repeatedly generating random samples to use as inputs, named after a Monaco city and gambling mecca.
This technique can be implemented by moving particles in each direction at a series of time steps by steps uniformly distributed between plus and minus square root quantity twice diffusion coefficient times time step to model diffusion.
{Monte Carlo} methods [prompt on {stochastic} methods or {random} methods]