What is the Best Python Library for Hidden Markov Models?

Hidden Markov Models (HMMs) are powerful types of statistical models used for modeling sequential data. They have found purposes in numerous fields, such as speech recognition, natural language processing, finance, and bioinformatics. Python, being a versatile programming language, provides a range of libraries for enforcing HMMs. In this article, we will discover unique Python libraries for HMMs, and evaluate their features, performance, and ease of use, sooner or later revealing the great choice for your needs.

A Primer on Hidden Markov Models

Before diving into the libraries, let's briefly recap the concept of HMMs. An HMM is a probabilistic model that represents a system transitioning between hidden states over time. It is composed of −

  •  A set of hidden states

  • An initial state probability distribution

  • A state transition probability matrix

  • An observation probability matrix

The primary goal is to infer the most probable sequence of hidden states given a sequence of observations.

Popular Python Libraries for HMMs

There are several Python libraries available for working with HMMs. Here, we focus on four popular choices −

  • HMMlearn

  • Pomegranate

  • GHMM

  • PyMC3

Let's discuss each of these libraries in detail.

a) HMMlearn

HMMlearn is a popular library for unsupervised learning and inference with HMMs. It is built on top of NumPy, SciPy, and scikit-learn, which are well-established libraries for scientific computing and machine learning in Python.

Key Features −

  • Simple interface for implementing Gaussian and Multinomial HMMs

  • Support for fitting and decoding algorithms, including Expectation-Maximization (EM) and Viterbi

Easily integrable with scikit-learn pipelines

Drawbacks −

  • Limited to Gaussian and Multinomial HMMs

  • No support for continuous emission distributions

b) Pomegranate

Pomegranate is a general-purpose probabilistic modeling library that supports HMMs, Bayesian networks, and other graphical models. It is designed to be flexible, fast, and easy to use.

Key Features −

  • Support for various types of HMMs, including discrete, Gaussian, and mixture models

  • Efficient algorithms for fitting, decoding, and sampling, using Cython for performance optimization

  • Parallelization support for model training and prediction

Drawbacks −

  • May have a steeper learning curve for beginners


The General Hidden Markov Model Library (GHMM) is a C library with Python bindings that provides an extensive set of tools for implementing HMMs. It is a well-established library with a long history.

Key Features −

  • Support for continuous and discrete emissions, including Gaussian, Poisson, and user-defined distributions

  • Wide range of algorithms for training, decoding, and evaluating HMMs

  • Support for higher-order HMMs and pair HMMs

Drawbacks −

  • Support for higher-order HMMs and pair HMMs

  • Requires additional effort to install and set up

d) PyMC3

PyMC3 is a popular library for Bayesian modeling and probabilistic machine learning. While not specifically tailored for HMMs, it provides a flexible framework for implementing them using Markov Chain Monte Carlo (MCMC) methods.

Key Features −

  • High-level interface for building complex Bayesian models

  • Efficient MCMC sampling using the No-U-Turn Sampler (NUTS) and other advanced algorithms

  • Theano-based computation for performance optimization and GPU support

Drawbacks −

  • More complex and less intuitive for HMM-specific tasks

  • MCMC methods may be slower and less efficient than specialized HMM algorithms

  • Theano dependency may cause compatibility issues, as it is no longer actively maintained

Comparison and Recommendations

Now that we have discussed the features and drawbacks of each library, let's compare them and determine the best choice for different use cases.

a) For beginners and simple HMM tasks: HMMlearn

If you are new to HMMs or working on a simple project with Gaussian or Multinomial HMMs, HMMlearn is an excellent choice. Its straightforward interface, built on top of familiar libraries like NumPy and scikit-learn, makes it easy to get started.

b) For advanced HMM tasks and performance: Pomegranate

Pomegranate is ideal for more complex HMM tasks and offers flexibility in modeling various types of HMMs. Its Cython implementation and parallelization support ensure high performance. However, it may have a steeper learning curve for beginners.

c) For specialized applications and legacy projects: GHMM

GHMM is well-suited for specialized applications like higher-order HMMs or pair HMMs, which may not be supported by other libraries. However, its lack of active maintenance and potential compatibility issues make it less suitable for new projects.

d) For Bayesian modeling enthusiasts: PyMC3

If you are familiar with Bayesian modeling and prefer MCMC methods, PyMC3 offers a powerful framework for implementing HMMs. However, its complex interface and slower MCMC algorithms may not be suitable for everyone or every project.


In summary, the best Python library for Hidden Markov Models depends on your specific needs, expertise, and project requirements. For most users, HMMlearn and Pomegranate offer the best balance between ease of use, flexibility, and performance. If your project requires more specialized features or Bayesian modeling, GHMM and PyMC3 may be more appropriate. Whichever library you choose, Python provides a rich ecosystem for working with HMMs and exploring their potential applications across various domains.

Updated on: 08-May-2023


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