Convergence and loss bounds for Bayesian sequence prediction
Date
2003
Authors
Hutter, Marcus
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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
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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
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Source
IEEE Transactions on Information Theory
Type
Journal article
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Open Access
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