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A toolkit for adaptive importance sampling featuring implementations of variational Bayes and population Monte Carlo.

Project Description

pypmc is a python package focusing on adaptive importance sampling. It can be used for integration and sampling from a user-defined target density. A typical application is Bayesian inference, where one wants to sample from the posterior to marginalize over parameters and to compute the evidence. The key idea is to create a good proposal density by adapting a mixture of Gaussian or student’s t components to the target density. The package is able to efficiently integrate multimodal functions in up to about 30-40 dimensions at the level of 1% accuracy or less. For many problems, this is achieved without requiring any manual input from the user about details of the function.

Useful tools that can be used stand-alone include:

  • importance sampling (sampling & integration)
  • adaptive Markov chain Monte Carlo (sampling)
  • variational Bayes (clustering)
  • population Monte Carlo (clustering)

Release history Release notifications

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1.1.2

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1.1.1

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1.1

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1.0

This version
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0.9

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pypmc-0.9.tar.gz (807.6 kB) Copy SHA256 hash SHA256 Source None Aug 20, 2014

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