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Stochastic chains with memory of variable length

Stochastic chains with memory of variable length are a family of stochastic chains of finite order in a finite alphabet, such as, for every time pass, only one finite suffix of the past, called context, is necessary to predict the next symbol. These models were introduced in the information theory literature by Jorma Rissanen in 1983,[1] as a universal tool to data compression, but recently have been used to model data in different areas such as biology,[2] linguistics[3] and music.[4]

  1. ^ Rissanen, J (Sep 1983). "A Universal Data Compression System". IEEE Transactions on Information Theory. 29 (5): 656–664. doi:10.1109/TIT.1983.1056741.
  2. ^ Bejenaro, G (2001). "Variations on probabilistic suffix trees: statistical modeling and prediction of protein families". Bioinformatics. 17 (5): 23–43. doi:10.1093/bioinformatics/17.1.23. PMID 11222260.
  3. ^ Galves A, Galves C, Garcia J, Garcia NL, Leonardi F (2012). "Context tree selection and linguistic rhythm retrieval from written texts". The Annals of Applied Statistics. 6 (5): 186–209. arXiv:0902.3619. doi:10.1214/11-AOAS511.
  4. ^ Dubnov S, Assayag G, Lartillot O, Bejenaro G (2003). "Using machine-learning methods for musical style modeling". Computer. 36 (10): 73–80. CiteSeerX 10.1.1.628.4614. doi:10.1109/MC.2003.1236474.
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