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Convergence and loss bounds for Bayesian sequence prediction

Date

Authors

Hutter, Marcus

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

The probability of observing xt at time t, given past observations x1 ⋯ xt-1 can be computed if the true generating distribution μ of the sequences x1x2x3 ⋯ is known. If μ is unknown, but known to belong to a class M one can base one's prediction on

Keywords

Keywords: Algorithms; Boundary conditions; Convergence of numerical methods; Decision theory; Function evaluation; Set theory; Bayesian sequence prediction; General loss function and bounds; Mixture distributions; Probability distributions Bayesian sequence prediction; Convergence; General loss function and bounds; Mixture distributions

URI

http://hdl.handle.net/1885/51174

Collections

ANU Research Publications

Source

IEEE Transactions on Information Theory

Type

Journal article

Access Statement

Open Access

DOI

10.1109/TIT.2003.814488

Downloads

File

Description

01_Hutter_Convergence_and_loss_bounds_2003.pdf (232.05 KB)