May 1, 2024
Updated May 9, 2025
22 minute read
Hidden Markov Models (HMMs) are a fascinating and powerful class of statistical models used to describe systems where you observe a sequence of events, but the underlying states that generate these events are not directly visible—they are "hidden." Imagine trying to figure out the weather (a hidden state) based only on whether someone is carrying an umbrella (an observable event). HMMs provide a mathematical framework to make these kinds of inferences. At a high level, an HMM assumes that the system transitions between these hidden states according to certain probabilities, and each hidden state has a probability of emitting specific observable symbols.
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Find a path to becoming a Hidden Markov Models. Learn more at:
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Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Hidden Markov Models.
This comprehensive textbook covers a wide range of topics in pattern recognition and machine learning, including HMMs. It provides a rigorous mathematical treatment of the subject matter, making it suitable for advanced students and researchers.
This specialized book focuses on the application of HMMs in bioinformatics. It covers topics such as sequence alignment, gene finding, and protein structure prediction. It is an excellent resource for researchers and practitioners in this field.
This classic book provides a comprehensive overview of statistical models for speech recognition, including HMMs. It is written by a leading researcher in the field and provides a deep understanding of the underlying theory and algorithms.
Focuses on the application of HMMs in time series analysis. It covers topics such as state space models, Kalman filtering, and Bayesian inference. It is suitable for researchers and practitioners seeking advanced methods for analyzing time series data.
This widely-used textbook covers a broad range of topics in natural language processing and speech recognition, including HMMs. It provides a clear and accessible introduction to the subject matter, making it suitable for both beginners and those seeking a broader perspective.
Focuses on the application of HMMs in natural language processing. It covers topics such as part-of-speech tagging, language modeling, and machine translation. It is suitable for researchers and practitioners seeking advanced methods for NLP tasks.
Explores the application of HMMs in computational biology. It covers topics such as sequence analysis, gene expression modeling, and protein structure prediction. It is suitable for researchers and practitioners seeking to apply HMMs to biological problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/qa9mrm/hidden
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