Monthly Archives: January 2014

machine learning versus statistics

My last two posts were both very much statistical in nature. The process of writing them has crystalized for myself the difference between statistics and machine learning. Statistics tends to be more focused on explaining and modeling data, where as … Continue reading

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p value and power

As a byproduct of getting into the science-wise FDR debate, I read Stephen Goodman’s interesting note, “A comment on replication, p values, and evidence.” It’s short and sweet. When we see a result with p value 0.05, we think that … Continue reading

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Science-wise FDR

I spent part of today engrossed in the animated discussion here¬†about what fraction of published results from bio-medical studies are false. A 2005 paper by Ioannidis based on simulations argued that 80% are false. A 2013 paper by Leek fitted … Continue reading

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pop science

There has been quite a bit of push backs against pop science (TED, Gladwell) recently. The main arguments are that they oversimplify, over-promise, and make claims not rigorously supported. The label middlebrow infotainment seems to sum it up. ¬†This is … Continue reading

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Statistical learning theory

I want to review some key concepts from statistical learning theory. My knowledge of it is very many influenced by the regularization viewpoint (due to the MIT class with Poggio). Let be training set of labeled examples. We want to … Continue reading

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