Announcing Computational Methods for Numerical Analysis

For about two years, I’ve been working on a book called Computational Methods for Numerical Analysis with R (CMNA), which will present an outline of numerical analysis topics with original (and simplified) implementations in R at a level appropriate for a graduate student or advanced undergraduate. Last night, I sent the latest draft to my editors, and I am quite pleased to say it should be heading into production, soon. The organizational structure of the text is based roughly on the organizational structure of MAPL 460 15-20 years ago: Introduction to Numerical Analysis Error Analysis Linear Equations Interpolation and Extrapolation

Deep Analytics and Big Data

Bill Marcus writes about the intersection between deep analytics and big data in HPE Insights: [T]he scale of an organization and its data is key to how much the process will matter, according to James Howard… “If you’re a small bank, machine learning is going to help get you through the day, but it’s not going to be something critical—whereas if you’re doing high-frequency trading, all you’re doing is machine learning.” Read more at HPE Insights: In the minds of machines: Fundamental change from deep analytics – HPE Business Insights By Bill Marcus, contributing writer In Munich, Germany, the technology

Of Course NaN^0 = 1

David Smith and I are now talking to each other in blog posts and it is only a little weird. Also, I’ve been traveling and am a bit behind. In a comment on this post, he notes this: I suspect the reason why R Core adopted the 0^0=1 definition is because of the binomial justification, R being a stats package after all. I can’t think of any defense for NaN^0=1 though… Well, it turns out there’s a good reason. If we go back C, and try an experiment, we can observe the following example produces these results: Compiling and executing

NaN versus NA in R

R has two different ways of representing missing data and understanding each is important for the user. NaN means “not a number” and it means there is a result, but it cannot be represented in the computer. The second, NA, explains that the data is just missing for unknown reasons. These appear at different times when working with R and each has different implications. NaN is distinct from NA. NaN implies a result that cannot be calculated for whatever reason, or is not a floating point number. Some calculations that lead to NaN, other than , are attempting to take

Things That Make You Go Hmm, Brookings Edition

Brookings questions the value of ban the box legislation and includes this gem: [E]mployers might prefer applicants with a college degree not because of what one learns in college but because earning that degree is correlated with greater motivation, intelligence, and diligence – unobservable characteristics that make you a more productive employee. If the characteristics are unobservable, how do we know they correlate with earning a degree or being a more productive employee? Image by xkcd, but you already knew that.