Explain the Hidden Markov Model.

1.	Explain the Hidden Markov Model.
Answer» The HIDDEN Markov model is a probabilistic model which is used to identify the probabilistic character of any event. It SAYS that an observed event is related to a set of probability distributions. If a system is being modeled into a Markov’s chain, then the main goal of HMM is to identify the hidden LAYERS of the Markov’s chain. Hidden means that the particular state is not observable to the observer. It is generally used for temporal DATA. HMM finds its application in reinforcement learning, temporal PATTERN recognition, etc. A Hidden Markov Model. Z₁…..Zₜ₊₁ are hidden states

Answer»

The HIDDEN Markov model is a probabilistic model which is used to identify the probabilistic character of any event. It SAYS that an observed event is related to a set of probability distributions. If a system is being modeled into a Markov’s chain, then the main goal of HMM is to identify the hidden LAYERS of the Markov’s chain. Hidden means that the particular state is not observable to the observer. It is generally used for temporal DATA. HMM finds its application in reinforcement learning, temporal PATTERN recognition, etc.

A Hidden Markov Model. Z₁…..Zₜ₊₁ are hidden states

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