dit: discrete information theory

dit is a Python package for discrete information theory.

For a quick tour, see the Quickstart. Otherwise, work your way through the various sections. Note that all code snippets in this documentation assume that the following lines of code have already been run:

In [1]: from __future__ import division # true division for Python 2.7

In [2]: import dit

In [3]: import numpy as np

Contents:

• General Information
  • Quickstart
• Notation
  • Basic Notation
  • Advanced Notation
• Distributions
  • Numpy-based ScalarDistribution
  • Numpy-based Distribution
• Operations
  • Marginal
  • Conditional
  • Join
  • Meet
  • Minimal Sufficient Statistic
• Finding Examples
• Optimization
  • Helper Functions
• Information Measures
  • Basic Shannon measures
  • Multivariate
  • Other Measures
  • Divergences
  • Secret Key Agreement
• Information Profiles
  • Shannon Partition and Extropy Partition
  • Complexity Profile
  • Marginal Utility of Information
  • Schneidman Profile
  • Entropy Triangle and Entropy Triangle2
  • Dependency Decomposition
• Rate Distortion Theory
  • Example
• Information Bottleneck
  • Example
• APIs
• Partial Information Decomposition
  • Background
  • Framework
  • Measures
  • Partial Entropy Decomposition
• References
• Changelog

Indices and tables

• Index
• Module Index
• Search Page