Hidden Markov models

Hidden Markov Models (HMMs) are a type of statistical Tool used in signal processing,speech recognition and other predictive technologies, such as natural language processing. HMMs are used to predict the likelihood of a certain sequence of events occurring in the future, based on models of previously observed sequences.

At its most basic level, a Hidden Markov Model (HMM) is a probabilistic sequence model which represents the probability of a sequence of states given a set of observations. The model consists of a finite set of underlying hidden states, and an observation sequence derived from those states. HMMs are used to represent many complex processes, such as understanding the structure of a transcription or the output of a language processing program.

The “Hidden” in HMM refers to the fact that the state transitions between different observations are never known; rather, they are inferred using a maximum likelihood estimate.

The most common applications of Hidden Markov Models involve its use in speech recognition technologies. By modeling speech according to hidden states, HMMs can then be used to distinguish between different sounds, words, and contexts. Speech recognition applications use HMMs to identify the patterns in a spoken phrase and then match those patterns to a database of previously-seen words and sequences.

Other applications of Hidden Markov Models include natural language processing (NLP), language understanding, biological sequence analysis, digital signal processing, computer vision and robotics.

HMMs are also used in fraud detection and e-commerce. By modeling customer behavior, HMMs can be used to detect unusual purchases or activities.

In summary, Hidden Markov Models are a powerful predictive tool, allowing the computer to make predictions about data with a high degree of accuracy. They are widely used in many fields of computing, including speech recognition, natural language processing, digital signal processing and robotics. HMMs have also found a home in the fields of fraud detection and e-commerce.