maxentropy：Python中的最大熵模型和最小散度模型.zip

# maxentropy: Maximum entropy and minimum divergence models in Python ## Purpose This package helps you to construct a probability distribution (Bayesian prior) from prior information that you encode as generalized moment constraints. You can use it to either: 1. find the flattest distribution that meets your constraints, using the maximum entropy principle (discrete distributions only) 2. or find the "closest" model to a given prior model (in a KL divergence sense) that also satisfies your additional constraints. ## Background The maximum entropy principle has been shown [Cox 1982, Jaynes 2003] to be the unique consistent approach to constructing a discrete probability distribution from prior information that is available as "testable information". If the constraints have the form of linear moment constraints, then the principle gives rise to a unique probability distribution of **exponential form**. Most well-known probability distributions are special cases of maximum entropy distributions. This includes uniform, geometric, exponential, Pareto, normal, von Mises, Cauchy, and others: see [here](https://en.wikipedia.org/wiki/Maximum_entropy_probability_distribution). ## Examples: constructing a prior subject to known constraints See the [notebooks folder](https://github.com/PythonCharmers/maxentropy/tree/master/notebooks). ### Quickstart guide This is a good place to start: [Loaded die example (scikit-learn estimator API)](https://github.com/PythonCharmers/maxentropy/blob/master/notebooks/Loaded%20die%20example%20-%20skmaxent.ipynb) ## History This package previously lived in SciPy (http://scipy.org) as ``scipy.maxentropy`` from versions v0.5 to v0.10. It was under-maintained and removed from SciPy v0.11. It has since been resurrected and refactored to use the scikit-learn Estimator inteface. ## Copyright (c) Ed Schofield, 2003-2019