Monthly Archives: January 2014

Configuration Spaces and the Meaning of Probability

I recently finished reading Nate Silver’s book The Signal and the Noise, which has gotten me thinking about how exactly one should interpret models/probability distributions, and the predictions they make. (If you’ve read this book or plan to read it, … Continue reading

Posted in Modeling | 8 Comments

Case Study 6: Digital images

In the last two posts, I described how we could generalize the notion of “tokens” that we first saw when analyzing non-numeric census data, to time series. In this context, a token is a short snippet from a time series … Continue reading

Posted in Feature extraction | 3 Comments