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Home » 2011 » August » 20 » Markov Models
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Markov Models

 Markov Models1. Description:   In probability theory, a Markov model is a stochastic model that assumes the Markov property. Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable.2. Key Points: 1. Set of states: 2. Process moves from one state to another generating a sequence of states : 3. Markov chain property:  probability of each subsequent state depends only on what was the previous state: 4. To define Markov model, the following probabilities have to be specified: transition probabilities and initial probabilities 3. Example of Markov Model:  1. Two states : ‘Rain’ and ‘Dry’.  2. Transition probabilities: P(‘Rain’|‘Rain’)=0.3 , P(‘Dry’|‘Rain’)=0.7 , P(‘Rain’|‘Dry’)=0.2, P(‘Dry’|‘Dry’)=0.8  3. Initial probabilities: say P(‘Rain’)=0.4 , P(‘Dry’)=0.6 .4. Calculation of sequence probability  1. By Markov chain property, probability of state sequence can be found by the formula:  2. Suppose we want to calculate a probability of a sequence of states in our example, {‘Dry’,’Dry’,’Rain’,Rain’}. P({‘Dry’,’Dry’,’Rain’,Rain’} ) = P(‘Rain’|’Rain’) P(‘Rain’|’Dry’) P(‘Dry’|’Dry’) P(‘Dry’)= 0.3*0.2*0.8*0.6
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