## Online Prediction - Bayes versus Experts

**Keywords:** Bayesian sequence prediction;
Prediction with Expert Advice;
general weights, alphabet and loss.

**Abstract:** We derive a very general regret bound in the framework of
prediction with expert advice, which challenges the best known
regret bound for Bayesian sequence prediction. Both bounds of the
form sqrt(Loss x complexity) hold for any
bounded loss-function, any prediction and observation spaces,
arbitrary expert/environment classes and weights, and unknown
sequence length.

### BibTeX Entry

@TechReport{Hutter:04bayespea,
author = "M. Hutter",
title = "Online Prediction -- Bayes versus Experts",
month = jul,
year = "2004",
note = "Presented at the EU PASCAL Workshop on
Learning Theoretic and Bayesian Inductive Principles (LTBIP-2004)",
url = "http://www.hutter1.net/ai/bayespea.htm",
ps = "http://www.hutter1.net/ai/bayespea.ps",
pdf = "http://www.hutter1.net/ai/bayespea.pdf",
slides = "http://www.hutter1.net/ai/sbayespea.pdf",
keywords = "Bayesian sequence prediction;
Prediction with Expert Advice;
general weights, alphabet and loss.",
abstract = "We derive a very general regret bound in the framework of
prediction with expert advice, which challenges the best known
regret bound for Bayesian sequence prediction. Both bounds of the
form $\sqrt{\mbox{Loss}\times\mbox{complexity}}$ hold for any
bounded loss-function, any prediction and observation spaces,
arbitrary expert/environment classes and weights, and unknown
sequence length.",
}