Publication

Mean field variational Bayesian inference for nonparametric regression with measurement error

Tung Huy Pham
2013
Journal Articles

Abstract

A fast mean field variational Bayes (MFVB) approach to nonparametric regression when the predictors are subject to classical measurement error is investigated. It is shown that the use of such technology to the measurement error setting achieves reasonable accuracy. In tandem with the methodological development, a customized Markov chain Monte Carlo method is developed to facilitate the evaluation of accuracy of the MFVB method. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

Official source

https://infoscience.epfl.ch/entities/publication/81256a9c-1b80-4b9a-835b-eea017f11a45

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Mean field variational Bayesian inference for nonparametric regression with measurement error

Chat with Graph Search

Euclid preparation: LIII. LensMC, weak lensing cosmic shear measurement with forward modelling and Markov Chain Monte Carlo sampling

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

A FEM modelling workflow to simulate THM effects in the rock around nuclear waste packages