Frequently Asked Questions(FAQs)

Q. 1 What is a Hidden Markov Model (HMM)?

A statistical model called a hidden markov model is used to describe systems that change between states with specific probabilities. The reason it is called “hidden” is that although the states produce observable outputs or emissions, they are not directly observable.

Q. 2 What are the key components of an HMM?

States, emission probabilities connected to each state, transition probabilities between states, and an initial probability distribution over states make up an HMM.

Q. 3 How is an HMM different from a regular Markov Model?

Every state in a standard Markov model can be observed directly. On the other hand, only the emissions-the observable outputs-are visible in an HMM, while the states remain hidden.

Q. 4 What are the applications of Hidden Markov Models?

Speech recognition, natural language processing, bioinformatics (gene prediction, for example), and many other fields where systems can be modeled as sequences of observable events with underlying hidden states are applications that heavily rely on HMMs.

A statistical model called a Hidden Markov Model (HMM) is used to describe systems with changing unobservable states over time. It is predicated on the idea that there is an underlying process with concealed states, each of which has a known result. Probabilities for switching between concealed states and emitting observable symbols are defined by the model.

Because of their superior ability to capture uncertainty and temporal dependencies, HMMs are used in a wide range of industries, including finance, bioinformatics, and speech recognition. HMMs are useful for modelling dynamic systems and forecasting future states based on sequences that have been seen because of their flexibility.