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September 15, 2020 4:25 pm 10 pts
ne etC. om the sorted array. cending order based o nding order based on distance values he most frequer class of thes rows Return the predicted class Hidden Markov Model: The hidden markov model is a finite se enciated with (generally multidimensional) probability distribution Transitions ened by a set ot probabilities called transition probabilities. In a particula ation can be generated, according to the associated probability state visible to an external observer and therefore states are hidde 5. associated observation can be gens ities calle probability dieti a finite set of states, each of which is istribution Transitions among are observation particular state an outcome or ds Soclated probability distribution. It is only the outcon ot the state name hidden markov model. are 'hidden' to the outside, hen In order to define on HMM completely, following elements are needed. The number of states of the model, N. ments are needed. The number of observation symbol in the alophabet M. if the observation ai then M is infinite. ous A set of state transition probabilities A = {a). a= Plq=i|9,= i), 1si, jsN where q, denotes the current state. Transition probability should satisfy the normal stochastic constraints, and a0, 1 si,jsN and 2a,=1 1sisN A probability distribution in each of the states B {b (k)} b(K) = P{a, = v, | q, = i). 1sj
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