Lately I’ve been writing maximum likelihood estimation code by hand for some economic models that I’m working with. It’s actually a fairly simple task, so I thought that I would write up the basic approach in case there are readers who haven’t built a generic estimation system before. First, let’s start with a toy example
In a world in which the price of calculation continues to decrease rapidly, but the price of theorem proving continues to hold steady or increase, elementary economics indicates that we ought to spend a larger and larger fraction of our time on calculation.1 Over the next ten years, I hope that more and more mathematically
Relevant to my earlier post about the relationship between direct democracy and laws prohibiting gay marriage, Pew Research just published poll data showing that a majority of Americans disapprove of same-sex marriage.
H. G. Wells famously said that, “statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” I think we’re getting closer to that day: even the Supreme Court of the United States plan...
Given the recent votes on same-sex marriage in New Jersey and Portugal, I wanted to test a seemingly innocuous claim that touches upon very broad issues in political theory: does the degree of directness of a “democratic” vote predict whether the vote will promote or prohibit same-sex marriage? Naively, it seemed clear to me that
In further support of the claim that a lot of deaths are partly self-induced, here’s a fascinating piece by Wired on the extraordinary rise in the percent of deaths among the young caused by their own poor decisions. It’s remarkable that, for the young, modern science has already made the world so safe that humanity,
My apologies for the long break between posts. Before the end of this week I’ll return to my series of posts on image processing in R. In the intervening time, I’ve finished a piece of code that I’d like to officially release to the public. The code in question is a very minimal object-relational mapper
This post is a continuation of my series dealing with matrix operations for image processing. My next goal is to demonstrate the construction of simple low-pass and high-pass spatial frequency filters in R. It’s easy enough to construct simple versions of these filters in R using the Fast Fourier Transform (also known as the FFT),
Over the next few posts, I’m going to be reviewing the use of R to implement the most commonly used matrix techniques for image manipulation. The code will be surprisingly simple to understand, because the real magic behind these techniques lies in the mathematics that R provides an abstract interface to. To start, I’m going
Lately, I’ve been running a series of fMRI experiments on visual perception. In the interests of understanding the underlying properties of the images I’m using as stimuli, I’ve been trying to learn more about the matrix transformations commonly used for image compression and image manipulation. Thankfully, R provides simple-to-use implementations for all of the matrix
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
