Model selection for variable length Markov chains and tuning the context algorithm

DOI10.1023/A:1004165822461zbMath0997.62060OpenAlexW2136209011MaRDI QIDQ1585889

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Publication date: 14 November 2002

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1004165822461

zbMATH Keywords

bootstrapresamplingKullback-Leibler informationfinal prediction errorzero-one lossfinite-memory sourceFSMX modelL2 lossoptimal tree pruningtree model

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Markov processes: estimation; hidden Markov models (62M05)

Related Items (6)

Modelling discrete longitudinal data using acyclic probabilistic finite automata ⋮ Context tree selection and linguistic rhythm retrieval from written texts ⋮ Rule generation for categorical time series with Markov assumptions ⋮ Bayesian analysis of variable-order, reversible Markov chains ⋮ Unnamed Item ⋮ Estimation of General Stationary Processes by Variable Length Markov Chains

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