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MidsizeBlowfish

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Mathematician, data scientist, teacher, dog lover

www.austinrochford.com

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www.austinrochford.com 11y ago

Reservoir Sampling for Streaming Data

MidsizeBlowfish
1pts0
www.austinrochford.com 12y ago

Verifying Typeclass Laws in Haskell with QuickCheck

MidsizeBlowfish
2pts0
www.austinrochford.com 12y ago

Duplicating Spheres and the Banach-Tarski Paradox

MidsizeBlowfish
3pts0
www.austinrochford.com 12y ago

Matrix Diagonalization and the Fibonacci Numbers

MidsizeBlowfish
1pts0
www.austinrochford.com 12y ago

Nonparametric Bayesian Progression with Gaussian Processes

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1pts0
statsbylopez.wordpress.com 12y ago

An inside look at the Sloan Sports Analytics Conference research paper contest

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1pts0
www.austinrochford.com 12y ago

Euler's Formula in sympy

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3pts0
www.austinrochford.com 12y ago

The Mathematics of Building a Heap in Linear Time

MidsizeBlowfish
2pts0
www.austinrochford.com 12y ago

The Importance of Sequential Testing

MidsizeBlowfish
19pts6
www.austinrochford.com 12y ago

There Are Almost No Rational Numbers

MidsizeBlowfish
2pts1
www.austinrochford.com 12y ago

Polynomial Regression and the Importance of Cross-Validation

MidsizeBlowfish
6pts1
www.austinrochford.com 12y ago

Generating Functions and Fibonacci Numbers

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24pts10
www.austinrochford.com 12y ago

The Median-of-Medians Algorithm

MidsizeBlowfish
115pts31
www.austinrochford.com 12y ago

Generalized Composition in Haskell

MidsizeBlowfish
2pts0
www.austinrochford.com 12y ago

My Most Interesting Interview Problem

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36pts51
www.austinrochford.com 12y ago

Prior Distributions for Bayesian Regression Using PyMC

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2pts0
www.austinrochford.com 13y ago

Bayesian Hypothesis Testing with PyMC

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1pts0
www.austinrochford.com 13y ago

Probability and Deuces in Tennis

MidsizeBlowfish
1pts0

There is also the interesting interpretation of logistic regression as a latent choice/utility model, where unobserved variation in utility is modeled using the logistic distribution. In this interpretation, the sigmoid function arises as the CDF of the logistic distribution.

This interpretation is also nice as it shows the natural relationship between logistic and probit regression. Probit regression arises when the unobserved utility is modeled with a Gaussian distribution.

A PhD worth getting should not involve you paying tuition. Much of the time your tuition is waived and you earn a small stipend (just enough to live frugally off of) teaching/working in a lab.

From a university's perspective, grad students are cheap labor that can eat a lot of sections of low level undergrad classes.

Haha, yes, I am the author. I do have a very formal style, an unfortunate product of too many years in grad school.

The SPRT is a nice toy test, but only useful for point hypotheses. There are generalizations to more realistic composite hypotheses, though.

This post is fantastic and the paper is fascinating. I particularly enjoyed his writing:

This is why God, in Her wisdom and mercy, gave us the bootstrap.

In the cases where I've had to use Hadoop, this was exactly what happened. We received a huge volume of data daily, which was aggregated by a nightly Hadoop job into a much more manageable amount of data for us to analyze with python/pandas.

Poster here, I actually had considered adding both of these methods to the end of the post, but decided to keep it focused on the problem itself.

edit

Maybe I'll write about them in a follow-up post.