computer science

PyMC

PyMC is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand PyMC rather than just read about it. In short: PyMC (formerly known as PyMC3) is a probabilistic programming library for Python. It can be used for Bayesian statistical modeling and probabilistic machine learning.

Key takeaways

PyMC belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect PyMC to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of PyMC from memory before moving on to harder problems.

Reference excerpt

PyMC (formerly known as PyMC3) is a probabilistic programming library for Python. It can be used for Bayesian statistical modeling and probabilistic machine learning. PyMC performs inference based on advanced Markov chain Monte Carlo and/or variational fitting algorithms. It is a rewrite from scratch of the previous version of the PyMC software. Unlike PyMC2, which had used Fortran extensions for performing computations, PyMC relies on PyTensor, a Python library that allows defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays. From version 3.8 PyMC relies on ArviZ to handle plotting, diagnostics, and statistical checks. PyMC and Stan are the two most popular probabilistic programming tools. PyMC is an open source project, developed by the community and has been fiscally sponsored by NumFOCUS. PyMC has been used to solve inference problems in several scientific domains, including astronomy, epidemiology, molecular biology, crystallography, chemistry, ecology and psychology. Previous versions of PyMC were also used widely, for example in climate science, public health, neuroscience, and parasitology. After Theano announced plans to discontinue development in 2017, the PyMC team evaluated TensorFlow Probability as a computational backend, but decided in 2020 to fork Theano under the name Aesara. Large parts of the Theano codebase have been refactored and compilation through JAX and Numba were added. The PyMC team has released the revised computational backend under the name PyTensor and continues the development of PyMC.

Inference engines PyMC implements non-gradient-based and gradient-based Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference and stochastic, gradient-based variational Bayesian methods for approximate Bayesian inference.

MCMC-based algorithms: No-U-Turn sampler (NUTS), a variant of Hamiltonian Monte Carlo and PyMC's default engine for continuous variables Metropolis–Hastings, PyMC's default engine for discrete variables Sequential Monte Carlo for static posteriors Sequential Monte Carlo for approximate Bayesian computation Variational inference algorithms: Black-box Variational Inference

See also Stan is a probabilistic programming language for statistical inference written in C++ ArviZ a Python library for exploratory analysis of Bayesian models Bambi is a high-level Bayesian model-building interface based on PyMC List of open-source mathematical libraries

References

Further reading Martin, Osvaldo (2024). Bayesian Analysis with Python: A Practical Guide to Probabilistic Modeling (Third ed.). Packt. ISBN 978-1-80512-716-1.

External links PyMC website PyMC source, a Git repository hosted on GitHub PyTensor is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.

Illustrations

Worked examples

Example 1 — a first encounter with PyMC

Start with the simplest possible case. Write down what PyMC claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to PyMC before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about PyMC ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of PyMC

In research: PyMC appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses PyMC in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: PyMC is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computational statistics, Free Bayesian statistics software, Monte Carlo software, so understanding it makes those chapters shorter.
In everyday life: Look for PyMC outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study PyMC in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what PyMC means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain PyMC out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is PyMC in simple terms?

PyMC (formerly known as PyMC3) is a probabilistic programming library for Python. It can be used for Bayesian statistical modeling and probabilistic machine learning.

Why does PyMC matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study PyMC?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on PyMC.