Skip to main content

Compute the Kiefer-Wolfowitz nonparametric maximum likelihood estimator

Project description

Kiefer-Wolfowitz Nonparametric Empirical Bayes

Compute the Kiefer-Wolfowitz nonparametric maximum likelihood estimator for mixtures.

In contrast to the previous approaches, the optimization problem is reformulated into a convex problem by Koenker and Mizera (2014)'s method and efficiently solved by interior-point method.

Making Predictions With No Features - A Basic Usage

Given a training set T = {y_i}, the algorithm provides a way to construct a predictor of future y-values such that the sum of squared errors between observations and predictors is minimized.

Getting Started


You will need:

  • python (>= 3.6)
  • pip (>= 19.0.3)
  • MOSEK (>=8.1.30)

Important about MOSEK:

  • MOSEK is a commercial optimization software. Please visit MOSEK for license information.
  • PIP:
pip install -f Mosek --user

For different ways of installation, please visit their installation page.

  • MOSEK needs to be installed in the GLOBAL environment.


pip install kwnpeb


  • simple - The basic usage
  • bayesball - In-season prediction of batting averages with the 2005 Major League baseball



This project is licensed under the MIT License - see the file for details

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for kwnpeb, version 0.1.11
Filename, size File type Python version Upload date Hashes
Filename, size kwnpeb-0.1.11-py3-none-any.whl (4.5 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size kwnpeb-0.1.11.tar.gz (3.4 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page