- The algorithm begins by determining the initial likelihoods for each state, based on the first observation.
- For each subsequent observation, the algorithm calculates the probability of being in each state by using the new observation and the previous state probabilities.
HMM viterbi algorithm linked to values
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Hi all,
I have used the HMM toolbox, using 4 states (Each states can take numbers from 1 to 6).
Then I apply the viterbi algortihm and find the optimum path of states. However, my question is:
How then the viterbi states of a given sequence are linked to certain values? here are the input data
A=[0.6 0.2 0.1 0.1;0.1 0.6 0.15 0.15; 0.15 0.15 0.6 0.1; 0.1 0.1 0.25 0.55]; %transition matrix
emis = [0.025 0.135 0.34 0.34 0.135 0.025;0.025 0.135 0.34 0.34 0.135 0.025;0.025 0.135 0.34 0.34 0.135 0.025;0.025 0.135 0.34 0.34 0.135 0.025]; %emission matrix, probabilities follow gaussian distribution (six bins of probabilities)
Initial_vales= [ 0.25 0.32 0.58 0.42 ..0.8]; %initial data given (24*20), range from 0.1 to 1.8!
%states_1= 0-0.25
%state_2=0.25-0.50
%state_2=0.5-0.75
%state_4=>0.75
thanks in advance,
Nikolas
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Aneela
el 16 de Feb. de 2024
Hi Nikolas Spiliopoulos,
The Viterbi algorithm generates the most likely sequence of the hidden states (Viterbi States) in “HMM (Hidden Markov Models)”.
The Viterbi states of a given sequence are linked to the certain values through the emission probabilities of the observed value and the transition probabilities from the previous state.
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