.. _example_algorithms_plot_bayesian_blocks:


Bayesian Blocks for Histograms
------------------------------
.. currentmodule:: astroML

Bayesian Blocks is a dynamic histogramming method which optimizes one of
several possible fitness functions to determine an optimal binning for
data, where the bins are not necessarily uniform width.  The astroML
implementation is based on [1]_.  For more discussion of this technique,
see the blog post at [2]_.

The code below uses a fitness function suitable for event data with possible
repeats.  More fitness functions are available: see :mod:`density_estimation`

References
~~~~~~~~~~
.. [1] Scargle, J `et al.` (2012)
       http://adsabs.harvard.edu/abs/2012arXiv1207.5578S
.. [2] http://jakevdp.github.com/blog/2012/09/12/dynamic-programming-in-python/



.. rst-class:: horizontal


.. image:: ../images/algorithms/plot_bayesian_blocks_1.png
    :align: center
    :scale: 100



.. image:: ../images/algorithms/plot_bayesian_blocks_2.png
    :align: center
    :scale: 100



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.. literalinclude:: plot_bayesian_blocks.txt

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**Python source code:**

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:download:`[download source: plot_bayesian_blocks.py] <plot_bayesian_blocks.py>`

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