Hidden Markov Models (HMMs) - Python for Integrated Circuits - - An Online Book - |
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Python for Integrated Circuits http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= Hidden Markov Models (HMMs) are a type of probabilistic model used in various fields, including speech recognition, natural language processing, bioinformatics, and more. HMMs are particularly useful for modeling systems that exhibit temporal or sequential behavior, where the underlying processes are assumed to be hidden and can only be inferred from observable data. Here are the key components of a Hidden Markov Model:
The key idea behind HMMs is that you have a sequence of observations and you want to infer the most likely sequence of hidden states that generated these observations. This is done using two fundamental problems:
HMMs are versatile and have been applied to a wide range of applications. For example, in speech recognition, HMMs can model phonemes as states and use them to recognize spoken words. In bioinformatics, HMMs are used for sequence alignment and gene prediction. In finance, they can be used for modeling stock price movements, and in natural language processing, they are used for tasks like part-of-speech tagging and named entity recognition. ============================================
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