My experience with applied statistics has been fun in large part because it involved making the Bayesian rubber meet the road of inherent computational complexity, and because it allowed me to define and explore theoretical concepts and tie them to our applied problem... I also had a good time formalizing and unifying ideas, making them more ( Read more... )

NeedleBase, by ITA Software*, seems to be a practical way of building a database from unstructured sources. Besides the web scraping (Information Extraction), they have tools for data cleaning/merging, all while maintaining provenance information (i.e. every datum points to its original source). video tutorial

Dapper's Data Mapper is great for ( Read more... )

According to the sources I've read, Gibbs sampling doesn't really tell you how to sample... it's just divide-and-conquer: you still need to sample each variable from its full conditional ( Read more... )

"Data Mining" and "Machine Learning" are separate communities, which means they go to different conferences, etc. (I didn't meet any data mining people at NIPS). I would like to understand why, since the central idea is the same: do statistics in order to answer queries ( Read more... )

I'm wondering whether there's a meaningful connection between MDL and VLMCs.

I decided look up the definition of VLMCs. So I did a Google search for "Variable-length Markov Chain" + "prefix code", which led me to this page:


( Read more... )

L2 regularization is seen a way to avoid overfitting when doing regression, no more ( Read more... )

Why do we care about finding MLE, MAP parameter-settings? The focus on the maximum reminds me of the mode, a statistic that one doesn't care about most of the time. (Instead, one tends to focus on the mean and median).

Take the graph below:


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I sometimes read a paper, and think: how I wish I had someone around here to work on this stuff with ( Read more... )

Chatting with Andrew tonight, at the CS grad lounge, we talked about Hit Song Science and we eventually focused on the following tangent ( Read more... )

Herb Simon, and his student Pat Langley have long been interested in comparing human-learning and machine-learning side-to-side ( Read more... )