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Added the "Markov-chain forecasting models"-part, with some examples |
m →Tolerant Markov model: HTTP → HTTPS for IEEE Xplore, replaced: http://ieeexplore.ieee.org/abstract/ → https://ieeexplore.ieee.org/abstract/ |
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==Tolerant Markov model==
A Tolerant Markov model (TMM) is a probabilistic-algorithmic Markov chain model.<ref name="TMMs">{{cite book |first1=D. |last1=Pratas |first2=M. |last2=Hosseini |first3=A. J. |last3=Pinho |chapter=Substitutional tolerant Markov models for relative compression of DNA sequences |title=PACBB 2017 – 11th International Conference on Practical Applications of Computational Biology & Bioinformatics, Porto, Portugal |pages=265–272 |year=2017 |doi=10.1007/978-3-319-60816-7_32 |isbn=978-3-319-60815-0}}</ref> It assigns the probabilities according to a conditioning context that considers the last symbol, from the sequence to occur, as the most probable instead of the true occurring symbol. A TMM can model three different natures: substitutions, additions or deletions. Successful applications have been efficiently implemented in DNA sequences compression.<ref name="TMMs" /><ref name="GECO">{{cite book |first1=D. |last1=Pratas |first2=A. J. |last2=Pinho |first3=P. J. S. G. |last3=Ferreira |chapter-url=
==Markov-chain forecasting models==
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