Monte Carlo (MC) methods are well-known computational techniques applied in different fields (as finance, biology, signal processing, machine learning) for Bayesian inference and stochastic optimization. Although they are very flexible methodologies and their applicability is almost unlimited, several drawbacks are still unsolved, specially for high dimensional problems and large data analysis. The main limitation is related to the computational cost and the time needed to obtain reliable results. In this project, we propose to investigate the combination of two main MC approaches, the importance sampling (IS) and the Markov Chain Monte Carlo (MCMC) schemes, considering jointly the use of parallel and population techniques. The developed methodology is intended to be applied to big industrial and medical data.
News published in Agência FAPESP Newsletter about the scholarship: