Hidden Markov Models (HMM) in Artificial intelligence

A hidden Markov model (HMM) allows us to talk about both observed events (like words that we see in the input) and hidden events (like part-of-speech tags) that we think of as causal factors in our probabilistic model. An HMM is specified by the following components

Hidden Markov Models (HMM)
Hidden Markov Models (HMM)

A first-order hidden Markov model instantiates two simplifying assumptions

First, as with a first-order Markov chain, the probability of a particular state depends only on the previous state:

Markov Assumption: P(qi |q1…qi−1) = P(qi |qi−1)

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