Search Results for author:
Found 5 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.
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.
Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises
Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years.
Adaptive Independent Sticky MCMC algorithms
In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky MCMC algorithms, for efficient sampling from a generic target probability density function (pdf).
