Oct 2, 2017 · post

Probabilistic programming: an annotated bibliography

Earlier this year we launched a research report on probabilistic programming, an emerging programming paradigm that makes it easier to describe and train probabilistic models. The Bayesian probabilistic approach to model building and inference has many advantages in practical data science, including the ability to quantify risk (a superpower in industries like finance and insurance) and the ability to insert institutional knowledge (which is particularly useful when data is scarce). The rise of probabilistic programming languages has made it a more practical technique for time-constrained working data scientists.

If that sounds great to you, and you’re looking to learn more, the first thing you can do is — work with us! We’ll be glad to discuss our report, relevant use cases in your industry, and next steps to incorporate this approach into your data science work.

But you might also enjoy this list of our favorite resources for learning how to do Bayesian inference and build probabilistic programming systems. These are the books, papers and tutorials we found most useful when conducting our research.

Practical books

If you’re just starting out then we recommend either Doing Bayesian Data Analysis by John Kruschke, or Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson-Pilon.

Krushke’s book uses R and Stan (and a language called JAGS, that is really only used for teaching these days). Davidson-Pilon uses Python and PyMC. Choose between these books based on your language preferences. If you don’t have a language preference, we at Fast Forward Labs recommend Davidson-Pilon’s book, which is available online, and in particular the PyMC3 edition (there are some important differences between PyMC3 and previous versions).

Theoretical books

The practical books above cover the basics of the theoretical and mathematical side, but if you’d like a deeper dive into why we do what we do, we recommend Data Analysis: A Bayesian Tutorial by Sivia and Skilling. It’s a relatively short and extremely clear book. For an even shorter introduction, we love Brendon Brewer’s lecture notes for STATS 331.

If your background is in economics or life sciences, you may prefer Data Analysis Using Regression and Multilevel/Hierarchical Models by Gelman and Hill. If your background is in physics or engineering, you may prefer Principals of Data Analysis by Prasenjit Saha (which is available free online).


If you’d like a reading list of research papers, there is no better place to start than the excellent annotated bibliography published last year by Alexander Etz and colleagues. Their notes place the research in a historical and conceptual context, so this is in a sense the least technical document in this list. But the papers they discuss are academic research, so you’ll be grappling with some big ideas, including our favorite

The probability that a person is dead (i.e., data) given that a shark has bitten the person’s head off (i.e., theory) is 1. However, given that a person is dead, the probability that a shark has bitten this person’s head off is very close to zero”.

If you’re interested in the algorithmic and computational cutting edge (Hamiltonian Monte Carlo, variational methods, etc.) then we have a blog post that links to a selection of important papers.

Tutorials and articles

Finally, here are a selection of shorter and/or use-case specific practical articles we’ve found interesting and useful:

Read more

Oct 4, 2017 · post
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Oct 20, 2022 · newsletter

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Sep 21, 2022 · newsletter

CFFL September Newsletter

September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance. more
Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books. more
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August 2022 Welcome to the August edition of the Cloudera Fast Forward Labs newsletter. This month we’re thrilled to introduce a new member of the FFL team, share TWO new applied machine learning prototypes we’ve built, and, as always, offer up some intriguing reads. New Research Engineer! If you’re a regular reader of our newsletter, you likely noticed that we’ve been searching for new research engineers to join the Cloudera Fast Forward Labs team. more

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