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Statements

Subject Item
dbr:Variable-order_Markov_model
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Variable-order Markov model
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In the mathematical theory of stochastic processes, variable-order Markov (VOM) models are an important class of models that extend the well known Markov chain models. In contrast to the Markov chain models, where each random variable in a sequence with a Markov property depends on a fixed number of random variables, in VOM models this number of conditioning random variables may vary based on the specific observed realization.
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dbr:Statistical_classification dbr:Haplotyping dbr:Sequence_analysis_in_social_sciences dbr:Code dbr:Bioinformatics dbr:Prediction dbr:Conditional_probability dbc:Markov_models dbr:Exponential_growth dbr:Artificial_intelligence dbr:Data_compression dbr:Markov_chain_Monte_Carlo dbr:Markov_chain dbr:Machine_learning dbr:DNA dbr:Statistical_process_control dbr:Semi-Markov_process dbr:Markov_property dbr:Alphabet dbr:Variable_order_Bayesian_network dbr:Conditional_distribution dbr:Statistical_analysis dbr:Markov_process dbr:Random_variable dbr:Variance dbr:Spam_filtering dbr:Information_theory dbr:Examples_of_Markov_chains dbr:Stochastic_processes dbr:Protein dbr:Probability dbr:Stochastic_chains_with_memory_of_variable_length
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In the mathematical theory of stochastic processes, variable-order Markov (VOM) models are an important class of models that extend the well known Markov chain models. In contrast to the Markov chain models, where each random variable in a sequence with a Markov property depends on a fixed number of random variables, in VOM models this number of conditioning random variables may vary based on the specific observed realization. This realization sequence is often called the context; therefore the VOM models are also called context trees. VOM models are nicely rendered by colorized probabilistic suffix trees (PST). The flexibility in the number of conditioning random variables turns out to be of real advantage for many applications, such as statistical analysis, classification and prediction.
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