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Volumn 49, Issue 8, 2003, Pages 2061-2067

Convergence and loss bounds for Bayesian sequence prediction

Author keywords

Bayesian sequence prediction; Convergence; General loss function and bounds; Mixture distributions

Indexed keywords

ALGORITHMS; BOUNDARY CONDITIONS; CONVERGENCE OF NUMERICAL METHODS; DECISION THEORY; FUNCTION EVALUATION; SET THEORY;

EID: 0043268070     PISSN: 00189448     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIT.2003.814488     Document Type: Letter
Times cited : (25)

References (18)
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  • 6
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    • Proc. 12th European Conf. Machine Learning (ECML-2001), 2001 , pp. 239-250
    • Hutter, M.1
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    • Hutter, M.1
  • 8
    • 50949102173 scopus 로고    scopus 로고
    • Optimality of universal Bayesian prediction for general loss and alphabet
    • Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Manno (Lugano), Switzerland. [Online]; Tech. Rep. IDSIA-02-02
    • ____, (2002) Optimality of universal Bayesian prediction for general loss and alphabet. Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Manno (Lugano), Switzerland. [Online]. Available: ftp://ftp.idsia.ch/pub/techrep/IDSIA-02-02.ps.gz. Tech. Rep. IDSIA-02-02
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  • 9
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    • Solomonoff, R.J.1
  • 10
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    • The minimum description length principle and reasoning under uncertainty
    • Ph.D. dissertation, Univ. Amsterdam, Amsterdam, The Netherlands
    • P. D. Grünwald, "The minimum description length principle and reasoning under uncertainty," Ph.D. dissertation, Univ. Amsterdam, Amsterdam, The Netherlands, 1998.
    • (1998)
    • Grünwald, P.D.1
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    • M. Hutter, "New error bounds for Solomonoff prediction," J. Comput. Syst. Sci., vol. 62, no. 4, pp. 653-667, 2001. [Online]. Available: ftp://ftp.idsia.ch/pub/techrep/IDSIA-11-00.ps.gz.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.