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Turing machines, the number game, and inference
The number game, which stems from Josh Tenenbaum’s PhD thesis, illustrates some important ideas about Bayesian concept learning. Here is a discussion of this in lecture notes from Kevin Murphy for reference. The basic setup is as follows: I give you a set of numbers from 1 to 100, and you try to guess another number in the set. For instance, I give…
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Hello world from Google!
I haven’t updated the blog in a while, as I have been very busy interviewing, going back home for a bit, and transitioning into a new role. I’m proud to say that I’m now working in Mountain View, at the Googleplex! It’s an amazing opportunity to work with some of the top machine learning and statistics researchers…
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Calling R from Matlab – flat file communication
I showed earlier 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.So how can you pipe your data from Matlab to R and back? Flat file communication There are a variety of…
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Bayesian mixed effects model to estimate experimental parameters
I ran into a tricky problem while analyzing a series of experiments we recently performed with Scanbox. We’re trying to estimate the angle of the objective with respect to the cortical surface. The way we approach this is by scanning at a given depth, going down a little bit, scanning again, and so on. By…
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Scientific Python in the browser: ipython notebook
By far the most popular post on this blog is a review of several Python integrated development environments (IDEs) geared toward science. Coming from a Matlab background, it’s natural to search for something Matlab-like to replace it – an IDE with integrated editor, code execution, plotting, benchmarking, file management, etc. An increasingly attractive alternative is the IPython Notebook. The ipython Notebook…
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Be like Mike: Michael Jordan’s reading list
Not this Michael Jordan, that Michael Jordan. There’s a machine learning reading list by Michael Jordan that’s been floating around on Hacker News for a few years, and in a recent AMA he added a few more. Full list: Casella, G. and Berger, R.L. (2001). “Statistical Inference” Duxbury Press. Ferguson, T. (1996). “A Course in Large Sample Theory”…
xcorr: AI & neuro 