Lambda the Ultimate

I am pleased to announce that the latest issue of The Monad.Reader is now available.

The Monad.Reader is a quarterly magazine about functional programming. It is less-formal than journal, but somehow more enduring than a wiki page or blog post. I think it may be of interest to many of the readers of LtU.

Issue 7 consists of the following four articles:

• Matthew Naylor
  A Recipe for controlling Lego using Lava
• Yaron Minsky
  Caml Trading: Experiences in Functional Programming on Wall Street
• Duncan Coutts
  Book Review: “Programming in Haskell” by Graham Hutton
• Dimitry Golubovsky, Neil Mitchell, Matthew Naylor
  Yhc.Core – from Haskell to Core

Feedback and comments are very welcome.

Bayesian networks are one of the most important formalisms in contemporary machine learning. Given the structure of a network there are a number of well established inference algorithms, but the algorithms are quite involved to implement and applications are typically very performance sensitive. Hence the programmer faces a dilemma: quickly implement a solution in a high level language and suffer extended run time or grub around in C or Fortran for weeks but achieve good performance. This is a perfect application area for a DSL to neatly solve this dilemma, and this is exactly what AutoBayes does. Specify a high level description of the network and it generates high performance C++ code to solve it.

If you find this interesting you will also be interested in the links between probability theory and monads. Eric Kidd has had a great series of blog posts on this theme, and sigfpe has also posted on this theme. Those crazy academics have also been getting in on the action!