Informatics Report Series



Related Pages

Report (by Number) Index
Report (by Date) Index
Author Index
Institute Index

Title:Bayesian Estimators for Robins-Ritov's Problem
Authors: Stefan Harmeling ; Marc Toussaint
Date: 2007
Bayesian or likelihood-based approaches to data analysis became very popular in the field of Machine Learning. However, there exist theoretical results which question the general applicability of such approaches; among those a result by Robins and Ritov which introduce a specific example for which they prove that a likelihood-based estimator will fail (i.e.~it does for certain cases not converge to a true parameter estimate, even given infinite data). In this paper we consider various approaches to formulate likelihood-based estimators in this example, basically by considering various extensions of the presumed generative model of the data. We can derive estimators which are very similar to the classical Horvitz- Thompson and which also account for a priori knowledge of an observation probability function.
Links To Paper
1st Link
Bibtex format
author = { Stefan Harmeling and Marc Toussaint },
title = {Bayesian Estimators for Robins-Ritov's Problem},
year = 2007,
url = {},

Home : Publications : Report 

Please mail <> with any changes or corrections.
Unless explicitly stated otherwise, all material is copyright The University of Edinburgh