Markov Model in probability theory is a stochastic model used to model randomly changing systems where it is assumed that future states depend only on the current state not on the events that occurred before it (defined as the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in the fields of predictive modeling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property. There are four most common Markov models used in different situations, depending on whether every sequential state is observable or not, and whether the system is to be adjusted on the basis of observations made.

These are Markov chain (the simplest model), Hidden Markov Model (Markov chain with only part of states observable), Markov decision process (chain with applied action vector) and Hidden Markov decision process. There is also a Markov random field, or Markov network may be considered to be a generalization of a Markov chain in multiple dimensions, and Hierarchical Markov Models which can be applied to categorize human behavior at various levels of abstraction.

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