Simple and effective introduction to Bayes theorem and Hidden Markov Model (HMM)
Luis Serrano has explained the underlying concepts of Bayes theorem and HMM in a friendly manner in this video lecture.
Bayes theorem: It is a probabilistic approach used to calculate the probability of an event given the probability of another associated event.
Hidden Markov Model (HMM): It is a class of probabilistic models that helps us in unveiling a hidden sequence of events (finite set of events each representing a state) using an observed sequence of events (each observed event is emitted by a hidden state)
Bayes theorem: It is a probabilistic approach used to calculate the probability of an event given the probability of another associated event.
Hidden Markov Model (HMM): It is a class of probabilistic models that helps us in unveiling a hidden sequence of events (finite set of events each representing a state) using an observed sequence of events (each observed event is emitted by a hidden state)
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