Statistical Decisions Using Likelihood Information Without Prior Probabilities
Abstract
This paper presents a decision-theoretic approach to statistical inference that satisfies the Likelihood Principle (LP) without using prior information. Unlike the Bayesian approach, which also satisfies LP, we do not assume knowledge of the prior distribution of the unknown parameter. With respect to information that can be obtained from an experiment, our solution is more efficient than Wald's minimax solution. However, with respect to information assumed to be known before the experiment, our solution demands less input than the Bayesian solution.
Cite
Text
Giang and Shenoy. "Statistical Decisions Using Likelihood Information Without Prior Probabilities." Conference on Uncertainty in Artificial Intelligence, 2002.Markdown
[Giang and Shenoy. "Statistical Decisions Using Likelihood Information Without Prior Probabilities." Conference on Uncertainty in Artificial Intelligence, 2002.](https://mlanthology.org/uai/2002/giang2002uai-statistical/)BibTeX
@inproceedings{giang2002uai-statistical,
title = {{Statistical Decisions Using Likelihood Information Without Prior Probabilities}},
author = {Giang, Phan Hong and Shenoy, Prakash P.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2002},
pages = {170-178},
url = {https://mlanthology.org/uai/2002/giang2002uai-statistical/}
}