Real World Haskell. Chapter 4. Functional programming. Chapter 4. Functional programming Our early learning of Haskell has two distinct aspects. The first is coming to terms with the shift in mindset from imperative programming to functional: we have to replace our programming habits from other languages. We do this not because imperative techniques are bad, but because in a functional language other techniques work better. Our second challenge is learning our way around the standard Haskell libraries. As in any language, the libraries act as a lever, enabling us to multiply our problem solving power. In this chapter, we'll introduce a number of common functional programming techniques.
A simple command line framework In most of this chapter, we will concern ourselves with code that has no interaction with the outside world. -- file: ch04/InteractWith.hs -- Save this in a source file, e.g. 42 comments This is all we need to write simple, but complete, file processing programs. . $ . Some of the notation in our source file is new. 10 comments.
A gallery of interesting IPython Notebooks · ipython/ipython Wiki. Timeseries with Pandas. Bayesian Methods for Hackers. An intro to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view. Prologue The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. If Bayesian inference is the destination, then mathematical analysis is a particular path towards it.
Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Python v3.2.5 doc.