Hidden Markov Models (HMM)
A statistical model used to represent systems that are assumed to follow a Markov process with hidden states, commonly used in time series analysis.
Implications
A statistical model used to represent systems that transition between hidden (unobserved) states, often used in fields like speech recognition, bioinformatics, and finance to model sequences of observed events that depend on underlying states.
Example
Example: A speech recognition system uses a Hidden Markov Model to convert spoken language into text, modeling the transitions between phonemes based on observed audio signals.
Related Terms
Different from simple Markov models, which assume that all states are observable, HMMs deal with situations where the underlying states are hidden and must be inferred from observed data.
Get INSTANT comprehensive market research of any industry
Chat with AII Consultant Now!
