Search Results for author:
Found 13 papers, 0 papers with code
Spectral information criterion for automatic elbow detection
The elements of this subset are elbows of the error curve.
On the safe use of prior densities for Bayesian model selection
Furthermore, when the prior is improper, the marginal likelihood of the corresponding model is undetermined.
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning
This survey gives an overview of Monte Carlo methodologies using surrogate models, for dealing with densities which are intractable, costly, and/or noisy.
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.
Automatic tempered posterior distributions for Bayesian inversion problems
More specifically, we consider a Bayesian analysis for the variables of interest (i. e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power.
MCMC-driven importance samplers
Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference.
Deep Importance Sampling based on Regression for Model Inversion and Emulation
For this reason, several adaptive importance sampling (AIS) schemes have been proposed in the literature.
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.
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).
