| | |
| | |
Stat |
Members: 3643 Articles: 2'487'895 Articles rated: 2609
29 March 2024 |
|
| | | |
|
Article overview
| |
|
Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families | Peter Bartlett
; Peter Grunwald
; Peter Harremoes
; Fares Hedayati
; Wojciech Kotlowski
; | Date: |
19 May 2013 | Abstract: | We study online learning under logarithmic loss with regular parametric
models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction
strategy with Jeffreys prior and sequential normalized maximum likelihood
(SNML) coincide and are optimal if and only if the latter is exchangeable, and
if and only if the optimal strategy can be calculated without knowing the time
horizon in advance. They put forward the question what families have
exchangeable SNML strategies. This paper fully answers this open problem for
one-dimensional exponential families. The exchangeability can happen only for
three classes of natural exponential family distributions, namely the Gaussian,
Gamma, and the Tweedie exponential family of order 3/2. Keywords: SNML
Exchangeability, Exponential Family, Online Learning, Logarithmic Loss,
Bayesian Strategy, Jeffreys Prior, Fisher Information1 | Source: | arXiv, 1305.4324 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser claudebot
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |