Monads

Today I'm reading some background on Monad theory - as it relates to computer programming.

http://en.wikipedia.org/wiki/Functor

http://en.wikipedia.org/wiki/Adjoint_functors

http://en.wikipedia.org/wiki/Closure_operator

http://en.wikipedia.org/wiki/Kleisli_category

http://en.wikipedia.org/wiki/Continuation

http://lambda-the-ultimate.org/node/86

http://sanjaypande.blogspot.com/2004/06/understanding-scheme-continuations.html


http://www.java.net/external?url=http://blog.tmorris.net/understanding-monads-using-scala-part-1/

http://www.java.net/external?url=http://blog.sigfpe.com/2006/08/you-could-have-invented-monads-and.html

http://www.java.net/external?url=http://byorgey.wordpress.com/2009/01/12/abstraction-intuition-and-the-monad-tutorial-fallacy/

http://stackoverflow.com/questions/1823731/can-somebody-please-explain-this-continuation-in-scheme

Mathematics Genealogy Project

http://genealogy.math.ndsu.nodak.edu/

List of Mathematical Symbols

http://en.wikipedia.org/wiki/List_of_mathematical_symbols

The On-Line Encyclopedia of Integer Sequences (OEIS)

http://oeis.org/

Some new links...

http://en.wikipedia.org/wiki/Kelly_criterion

The original manuscript written by J.L. Kelly, Jr. in 1956, "A New Interpretation of Information Rate"
http://www.racing.saratoga.ny.us/kelly.pdf


http://en.wikipedia.org/wiki/St._Petersburg_paradox

SymPy: A Python library for symbolic mathematics
http://en.wikipedia.org/wiki/Sympy
http://sympy.org/
http://code.google.com/p/sympy/
http://planet.sympy.org/

mpmath: Python library for arbitrary-precision floating-point arithmetic
http://code.google.com/p/mpmath/

Sage: Open Source Mathematics Software
http://sagemath.org/

Scientific Tools for Python

http://www.scipy.org/

"SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge."

http://numpy.scipy.org/

NumPy is the fundamental package needed for scientific computing with Python. It contains among other things:

• a powerful N-dimensional array object
• sophisticated (broadcasting) functions
• tools for integrating C/C++ and Fortran code
• useful linear algebra, Fourier transform, and random number capabilities.

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Adventures in Data Land [interesting blog]

http://blog.smola.org/