What does a vector in a hidden Markov model mean?

1. What does a hidden Markov model show?
2. What are hidden variables in HMM?
3. What is the output of hidden Markov model?
4. What are the three fundamental problems that characterize a hidden Markov model?

What does a hidden Markov model show?

A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable. We call the observed event a `symbol' and the invisible factor underlying the observation a `state'.

What are hidden variables in HMM?

Hidden Markov Models (HMMs) are a class of probabilistic graphical model that allow us to predict a sequence of unknown (hidden) variables from a set of observed variables. A simple example of an HMM is predicting the weather (hidden variable) based on the type of clothes that someone wears (observed).

What is the output of hidden Markov model?

Hidden Markov models (HMMs) are sequence models. That is, given a sequence of inputs, such as words, an HMM will compute a sequence of outputs of the same length. An HMM model is a graph where nodes are probability distributions over labels and edges give the probability of transitioning from one node to the other.

What are the three fundamental problems that characterize a hidden Markov model?

HMM provides solution of three problems : evaluation, decoding and learning to find most likelihood classification.