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How does the Viterbi algorithm work?
The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models (HMM).
What are the basic components of Markov analysis?
The Markov analysis process involves defining the likelihood of a future action, given the current state of a variable. Once the probabilities of future actions at each state are determined, a decision tree can be drawn, and the likelihood of a result can be calculated.
What is Markov analysis study?
Markov analysis is a method used to forecast the value of a variable whose predicted value is influenced only by its current state, and not by any prior activity. Markov analysis is often used for predicting behaviors and decisions within large groups of people.
How does the Viterbi algorithm solve for the most likely sequence?
The Viterbi algorithm is an iterative approach to solving for the most likely sequence. Rather than finding the most probable hidden state sequence for all the observations, we just want to find the next most probable hidden state. We iteratively find the most probable states until we are done with the sequence of observations.
When to use conditional probability in the Viterbi algorithm?
If we have a set of states Q and a set of observations O, we are trying to find the state sequence that maximizes P (Q|O). By conditional probability, we can transform P (Q|O) to P (Q,O)/P (O), but there is no need in finding P (O) as P (O) does not pertain to changes in state sequences.
What does the emit _ p on Viterbi mean?
The emit_p represents how likely each possible observation, normal, cold, or dizzy is given their underlying condition, healthy or fever. If the patient is healthy, there is a 50% chance that he feels normal; if he has a fever, there is a 60% chance that he feels dizzy.