Variable-order Markov model: Difference between revisions

Revision as of 01:44, 23 January 2018

Revision as of 11:40, 23 January 2018

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Specifically, the learner generates a [[conditional distribution|conditional probability distribution]] <math>P(x_i\mid s)</math> for a symbol <math>x_i \in A</math> given a context <math>s\in A^*</math>, where the * sign represents a sequence of states of any length, including the empty context.

VOM models attempt to estimate [[conditional distribution]]s of the form <math>P(x_i\midsmid s)</math> where the context length <math>|s| \le D</math> varies depending on the available statistics.

In contrast, conventional [[Markov chain|Markov models]] attempt to estimate these [[conditional distribution]]s by assuming a fixed contexts' length <math>|s| = D</math> and, hence, can be considered as special cases of the VOM models.