Necessary and sufficient conditions of proper estimators based on self density ratio for unnormalized statistical models

DOI10.1016/j.neunet.2017.11.018zbMath1439.62206OpenAlexW2774771787WikidataQ47231183 ScholiaQ47231183MaRDI QIDQ2179310

Kazuyuki Hiraoka, Toshihiko Hamada, Gen Hori

Publication date: 12 May 2020

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2017.11.018

zbMATH Keywords

consistencyscore matchingnon-local scoring rulesself density ratiounnormalized statistical model

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Learning and adaptive systems in artificial intelligence (68T05) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (1)

A numerically stable algorithm for integrating Bayesian models using Markov melding

Uses Software

• darch

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Proper local scoring rules
• Local proper scoring rules of order two
• Some extensions of score matching
• Improving predictive inference under covariate shift by weighting the log-likelihood function
• Inference based on estimating functions in the presence of nuisance parameters. With comments and rejoinder
• Dimensionality reduction for density ratio estimation in high-dimensional spaces
• Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory
• Density Ratio Estimation in Machine Learning
• Introduction to Smooth Manifolds
• Training Products of Experts by Minimizing Contrastive Divergence
• A Two-Layer Model of Natural Stimuli Estimated with Score Matching
• Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
• Markov Random Field Modeling in Image Analysis
• Bounds on the Maximum Number of Factors in Designs with Two Distinct Groups of Factors
• Functional Bregman Divergence and Bayesian Estimation of Distributions
• Estimating Squared-Loss Mutual Information for Independent Component Analysis
• 10.1162/153244303322533223
• Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation
• A Fast Learning Algorithm for Deep Belief Nets

This page was built for publication: Necessary and sufficient conditions of proper estimators based on self density ratio for unnormalized statistical models