A Generic Ensemble Approach to Estimate Multidimensional Likelihood in Bayesian Classifier Learning

Abstract

In Bayesian classifier learning, estimating the joint probability distribution p(x,y) or the likelihood p(x|y) directly from training data is considered to be difficult, especially in large multidimensional data sets. To circumvent this difficulty, existing Bayesian classifiers such as Naive Bayes, BayesNet, and AηDE have focused on estimating simplified surrogates of p(x,y) from different forms of one‐dimensional likelihoods.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 09, 2015
Source ID
10.1111/coin.12063

Entities

People

  • Kai Ming Ting
  • Sunil Aryal

Organizations

  • Air Force Research Laboratory
  • Monash University

Tags

Readers

  • Computer Vision.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks