The Monte Carlo simulation estimates the probability of different outcomes in a process that cannot easily be predicted because of the potential for random variables.

This paper concerns the use of sequential Monte Carlo methods (SMC) for smoothing in general state space models. A well-known problem when applying the standard SMC technique in the smoothing mode is ...

We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in ...

Monte Carlo methods have become indispensable in simulating light transport due to their flexibility in handling complex phenomena such as scattering, absorption, and emission in heterogeneous media.

Learn how Value at Risk (VaR) predicts possible investment losses and explore three key methods for calculating VaR: ...