On the convergence speed of MDL predictions for bernoulli sequences

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3244, Issue , 2004, Pages 294-308

  Poland, Jan ^a   Hutter, Marcus ^a  

^a DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCE IDSIA   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; ERROR ANALYSIS; FORECASTING; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBABILITY; TURING MACHINES; ARTIFICIAL INTELLIGENCE;

BAYES MIXTURES; BERNOULLI DISTRIBUTIONS; KOLMOGOROV COMPLEXITY; MINIMUM DESCRIPTION LENGTH (MDL) PRINCIPLE; BERNOULLI SEQUENCES; CONVERGENCE WITH PROBABILITY ONE; HYPOTHESIS TESTING; MINIMUM DESCRIPTION LENGTH PRINCIPLE; PERFORMANCE MEASURE; RATE OF CONVERGENCE; SEQUENCE PREDICTION;

CONVERGENCE OF NUMERICAL METHODS; MIXTURES;

EID: 22944458209     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30215-5_23     Document Type: Conference Paper

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