An introduction to smoothing time series in python. Part III: Kalman Filter
Jun 2013 by Tiago Ramalho
If we have a mathematical model for the system in study, we can use that information to dramatically improve the quality of our prediction. Like in the previous filtering methods, we are taking advantage of the fact that to estimate the system state at some time $t$ we can use...
Preprint review: Parameter Space Compression Underlies Emergent Theories and Predictive Models
May 2013 by Tiago Ramalho
So here’s a preprint I found really interesting [arxiv:1303.6738]. I’ll try to give a quick overview of the story in my own words. The main concept used in the paper is the Fisher Information, which is no more than a measure of the curvature in the space of probability distributions....
Modeling excitable media with cellular automata
Apr 2013 by Tiago Ramalho
While researching for my seminar I came across a class of cellular automata which models spiral waves in excitable media. Because these models are so simple I had some fun implementing them in processing. Processing is great because you can use the javascript version to embed the visualization in a...
Using Beautiful Soup to convert Springpad notes to Evernote
Apr 2013 by Tiago Ramalho
A few months back I decided to migrate all my work notes from springpad to evernote because I found evernote more robust and simpler. I still keep my recipe collection on springpad though! Looks yummy. Anyway, surprisingly there were some scripts going from evernote to springpad but not the other...
An introduction to smoothing time series in python. Part II: wiener filter and smoothing splines
Apr 2013 by Tiago Ramalho
Wiener filter The wiener filter is a bit more advanced than the filters I previously covered, as it is the first one rooted in probability theory. Consider a more complicated measurement, $y = r*s + n$, where $R$ is an operator describing the response of the measurement equipment (for images, it is...