Hidden Markov Models

Abstract

Hidden Markov models (HMMs) are important in pattern recognition because they are ideally suited to classify patterns where each pattern is made up of a sequence of sub-patterns. For example, assume that a day is either sunny, cloudy, or rainy corresponding to three different types of weather conditions. Then a typical week during summer could be described as sunny, sunny, sunny, sunny, sunny, sunny, sunny corresponding to every day of the week being sunny. Similarly, it is possible that every day in a week during the rainy season can be rainy for which the week can be characterised as rainy, rainy, rainy, rainy, rainy, rainy, rainy.