What are Hidden Markov Models and how do they work?

What are Hidden Markov Models and how do they work?

Hidden Markov Models (HMMs) are statistical models used to represent systems that are assumed to be a Markov process with hidden states. They are widely used in various fields such as speech recognition, bioinformatics, and finance.

Can you explain the components of a Hidden Markov Model?

Sure! An HMM consists of states, observations, transition probabilities, emission probabilities, and an initial state distribution. The states are hidden, while the observations are visible.

What are some real-world applications of HMMs?

HMMs are used in speech recognition, part-of-speech tagging in natural language processing, and even in predicting stock prices.

How do you train a Hidden Markov Model?

Training an HMM typically involves using algorithms like the Baum-Welch algorithm, which is a type of Expectation-Maximization algorithm to estimate the model parameters.

What are the limitations of Hidden Markov Models?

Some limitations include the assumption of independence between observations and the difficulty in modeling long-range dependencies.