no code implementations • 25 Jul 2021 • D. Luengo, L. Martino, M. Bugallo, V. Elvira, S. Särkkä
MC methods proceed by drawing random samples, either from the desired distribution or from a simpler one, and using them to compute consistent estimators.
no code implementations • 6 May 2021 • F. Llorente, E. Curbelo, L. Martino, V. Elvira, D. Delgado
Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference.
no code implementations • 31 May 2020 • F. Llorente, L. Martino, V. Elvira, D. Delgado, J. López-Santiago
For the Gaussian case, we also provide a novel procedure for fitting the bandwidth parameter, in order to build a suitable emulator of a density function.
no code implementations • 10 Apr 2017 • L. Martino, V. Elvira, G. Camps-Valls
Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples.
no code implementations • 30 Jul 2015 • L. Martino, V. Elvira, D. Luengo, J. Corander, F. Louzada
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning.
no code implementations • 18 May 2015 • L. Martino, V. Elvira, D. Luengo, J. Corander
Monte Carlo methods represent the "de facto" standard for approximating complicated integrals involving multidimensional target distributions.