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Found 6 papers, 0 papers with code
A Survey of Monte Carlo Methods for Parameter Estimation
MC methods proceed by drawing random samples, either from the desired distribution or from a simpler one, and using them to compute consistent estimators.
MCMC-driven importance samplers
Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference.
Adaptive quadrature schemes for Bayesian inference via active learning
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.
Group Importance Sampling for Particle Filtering and MCMC
Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples.
Orthogonal parallel MCMC methods for sampling and optimization
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning.
Layered Adaptive Importance Sampling
Monte Carlo methods represent the "de facto" standard for approximating complicated integrals involving multidimensional target distributions.
