Monte Carlo methods are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results, i.e. by running simulations many times in succession in order to ...

This is a preview. Log in through your library . Abstract We consider Monte Carlo methods for the classical nonlinear filtering problem. The first method is based on a backward pathwise filtering ...

Monte Carlo methods have emerged as a crucial tool in the evaluation of measurement uncertainty, particularly for complex or non-linear measurement systems. By propagating full probability ...

When designing programs or software for the implementation of Monte Carlo (MC) hypothesis tests, we can save computation time by using sequential stopping boundaries. Such boundaries imply stopping ...

Monte Carlo methods have emerged as an indispensably robust tool for simulating particle transport in stochastic media, where material properties vary according to random processes. These techniques ...