Probability Approximation Resources
Do we even need probability approximations anymore? They’re not as necessary for numerical computation as they once were, but they remain vital for understanding the behavior of probability distributions and for theoretical calculations. Textbooks often leave out details such as quantifying the error when discussion approximations. The following pages are notes I wrote to fill in some of these details when I was teaching.
Python: Find the Highest Value in a Group
In my continued playing around with a “How I Met Your Mother” data set I needed to find out the last episode that happened in a season so that I could use it in a chart I wanted to plot.
Chapter 3 of Modeling data with functional programming in R is out
Chapter 3 of my book “Modeling data with functional programming in R” is available for download. This chapter describes map-vectorization and how it’s used in R. I make a distinction between different types of vectorization since f(x) = x^2 + 2*y – 5 is vectorized differently from sum(x). I call the first form map-vectorization, after the higher-order function map. In R, we call this apply, though conceptually they represent the same operation. This chapter uses the ebola.sitrep package I wrote as the basis for examples. The package downloads and parses PDF situation reports from the Health Ministries of Liberia and Sierra Leone. The parsing process is filled with examples of map operations, and I discuss numerous cases in the chapter. I also delve into some of the properties of map and how these can be used to reason about your code to ultimately implement your ideas succinctly.
UIUC free online courses on data mining starting on 9 Feb, lectured by Prof. Jiawei Han et al.
A series of free online data mining courses will start on 9 Feb 2015, lectured by Prof. Jiawei Han and several other staff at UIUC. Prof. Han is one of the top data mining researchers around the world, and has authored “Data Mining: Concepts and Technique”, one of the most popular data mining textbooks. Do not miss the opportunity if you are interested in learning data mining techniques.