In the last post I showed how to fit a Bayesian hierarchical model in R to estimate experimental parameters. Our main data analysis pipeline uses Matlab, and we’d like to integrate these two environments for the purpose of analysis. The example involved JAGS, which does have a direct Matlab interface in addition to the more common R one, but more … More Calling R from Matlab – flat file communication
I made a few ten-minute movies in Matlab composed of 1-second clips from BBC Earth, encoded as H.264, to use as visual stimuli. They played fine on Windows but would stop at the 170 second mark in Quicktime on Mac. We’re using Processing on Mac for visual display, which uses Quicktime in the background, so … More Fixing broken .m4v movies made in Matlab
Estimating a statistical model via maximum likelihood or MAP involves minimizing an error function – the negative log-likelihood or log-posterior. Generic functions built in to Matlab like fminunc and fmincon will often do the trick. There are many other free solvers available, which are often faster, or more powerful: Solvers by Mark Schmidt: there’s a huge collection of functions from Mark … More Off-the-shelf optimization functions in Matlab
It’s easy enough to load .mat files in Python via the scipy.io.loadmat function. But what about loading .pickle files into Matlab? That’s easy enough by calling a system command in Matlab, like so: Note that I’m setting the LD_LIBRARY_PATH environment variable since Matlab seems to reset this variable internally and it causes .so module import … More Load pickle files in Matlab
Previously, I showed how to whiten a matrix in Matlab. This involves finding the inverse square root of the covariance matrix of a set of observations, which is prohibitively expensive when the observations are high-dimensional – for instance, high-resolution natural images. Thankfully, it’s possible to whiten a set of natural images approximately by multiplying the … More Whiten images in Matlab
