Allen Downey

Allen Downey

Author of Think Bayes & Think Python

Bio: I am a Professor of Computer Science at Olin College in Needham MA, and the author of Think Python, Think Bayes, Think Stats and several other books related to computer science and data science. Previously I taught at Wellesley College and Colby College, and in 2009 I was a Visiting Scientist at Google, Inc. I have a Ph.D. from U.C. Berkeley and B.S. and M.S. degrees from MIT. Here is my CV. I write a blog about Bayesian statistics and related topics called Probably Overthinking It. Several of my books are published by O’Reilly Media and all are available under free licenses from Green Tea Press.

Python as a way of thinking

Python as a way of thinking

This article contains supporting material for this blog post at Scientific American.  The thesis of the post is that modern programming languages (like Python) are qualitatively different from the first generation (like FORTRAN and C), in ways that make them effective tools for teaching, learning, exploring, and thinking. I presented a longer version of this argument […]

More notebooks for Think Stats

More notebooks for Think Stats

More notebooks for Think Stats As I mentioned in the previous post, I am getting ready to teach Data Science in the spring, so I am going back through Think Stats and updating the Jupyter notebooks.  I am done with Chapters 1 through 6 now. If you are reading the book, you can get the notebooks by cloning this […]

New notebooks for Think Stats

New notebooks for Think Stats

Getting ready to teach Data Science in the spring, I am going back through Think Stats and updating the Jupyter notebooks.  When I am done, each chapter will have a notebook that shows the examples from the book along with some small exercises, with more substantial exercises at the end. If you are reading the […]

Millennials are Less Likely to Divorce

Millennials are Less Likely to Divorce

Millennials are getting married later than previous generations, as I wrote about here.  But the ones who get married are no more likely to divorce during the first 10 years, and after that they might be substantially less likely to get divorced. The following figure shows estimates for the fraction of people who have not […]

Millennials Are Still Not Getting Married

Millennials Are Still Not Getting Married

Last year I presented a paper called “Will Millennials Ever Get Married?” at SciPy 2015.  You can see video of the talk and download the paper here. I used data from the National Survey of Family Growth (NSFG) to estimate the age at first marriage for women in the U.S., broken down by decade of […]

It’s a Small World, Scale-free Network After All

It’s a Small World, Scale-free Network After All

Real social networks generally have the properties of small world graphs (high clustering and low path lengths) and the characteristics of scale free networks (a heavy-tailed degree distribution). The Watts-Strogatz (WS) network model has small world characteristics, but the degree distribution is roughly normal, very different from observed distributions. The Barabasi-Albert (BA) model has low […]

Bayes’s Theorem is Not Optional

Bayes’s Theorem is Not Optional

Abstract: I present a probability puzzle, the Rain in Seattle Problem, and use it to explain differences between the Bayesian and frequentist interpretations of probability, and between Bayesian and frequentist statistical methods.  Since I am trying to clear up confusion, I try to describe the alternatives without commenting on their pros and cons. Introduction Conversations […]

Does Trivers-Willard apply to people?

Does Trivers-Willard apply to people?

Editor’s note: Look forward to Allen’s talk in our conference talks section from Boston Data Fest and read more blogs by Allen here.  Does Trivers-Willard apply to people? Today I am working on another “one-day paper”, although this one is a bit of a cheat, since I’m a few hours past the deadline.  Nevertheless, the question of the day is whether […]

What is a distribution?

What is a distribution?

This article uses object-oriented programming to explore of one of the most useful concepts in statistics, distributions.  The code is in a Jupyter notebook. You can read a static version of the notebook on nbviewer. OR You can run the code in a browser by clicking this link and then selecting distribution.ipynb from the list. […]