Probablistic programming is an expressive and flexible way to build Bayesian statistical models in code. It's an entirely different mode of programming that involves using stochastic variables defined using probability distrib…
This post is part of a series covering the exercises from Andrew Ng's machine learning [https://www.coursera.org/course/ml] class on Coursera. The original code, exercise text, and data files for this post are available here […
In this blog post we'll examine some common techniques used in time series analysis by applying them to a data set containing daily closing values for the S&P 500 stock market index from 1950 up to present day. The objecti…
The topic of this post is perhaps a bit mundane, but after spending a considerable amount of time getting this right, I decided to put together a step-by-step guide so I would remember how to do it next time. And since I already w…
This post is part of a series covering the exercises from Andrew Ng's machine learning [https://www.coursera.org/course/ml] class on Coursera. The original code, exercise text, and data files for this post are available here […