2.0.10

The AdaMet Package
==================

**Adaptive Metropolis for Bayesian Analysis**

.. image:: https://img.shields.io/pypi/v/adamet.svg
        :target: https://pypi.org/project/adamet/
.. image:: https://img.shields.io/badge/arXiv-1208.3522-orange.svg
    :target: https://arxiv.org/abs/1208.3522
.. image:: https://img.shields.io/badge/DOI-10.1093/mnras/stt562-green.svg
    :target: https://doi.org/10.1093/mnras/stt562

AdaMet is a well-tested Python implementation by 
`Cappellari et al. (2013) <https://ui.adsabs.harvard.edu/abs/2013MNRAS.432.1709C>`_ 
of the Adaptive Metropolis algorithm by
`Haario H., Saksman E., Tamminen J. (2001) <https://doi.org/10.2307/3318737>`_.
It was used in several published papers in the astrophysics literature.

.. contents:: :depth: 2

Attribution
-----------

If you use this software for your research, please cite at least
`Cappellari et al. (2013)`_ where the implementation was introduced. 
The BibTeX entry for the paper is::

    @ARTICLE{Cappellari2013a,
        author = {{Cappellari}, M. and {Scott}, N. and {Alatalo}, K. and
            {Blitz}, L. and {Bois}, M. and {Bournaud}, F. and {Bureau}, M. and
            {Crocker}, A.~F. and {Davies}, R.~L. and {Davis}, T.~A. and {de Zeeuw},
            P.~T. and {Duc}, P.-A. and {Emsellem}, E. and {Khochfar}, S. and
            {Krajnovi{\'c}}, D. and {Kuntschner}, H. and {McDermid}, R.~M. and
            {Morganti}, R. and {Naab}, T. and {Oosterloo}, T. and {Sarzi}, M. and
            {Serra}, P. and {Weijmans}, A.-M. and {Young}, L.~M.},
        title = "{The ATLAS$^{3D}$ project - XV. Benchmark for early-type
            galaxies scaling relations from 260 dynamical models: mass-to-light
            ratio, dark matter, Fundamental Plane and Mass Plane}",
        journal = {MNRAS},
        eprint = {1208.3522},
        year = 2013,
        volume = 432,
        pages = {1709-1741},
        doi = {10.1093/mnras/stt562}
    }

Installation
------------

install with::

    pip install adamet

Without writing access to the global ``site-packages`` directory, use::

    pip install --user adamet

Documentation
-------------

The documentation is in the docstring of the file ``adamet.py``
or on `PyPi <https://pypi.org/project/adamet/>`_.

###########################################################################


AdaMet
======

AdaMet Purpose
--------------
This is the implementation by
`Cappellari et al. (2013) <https://ui.adsabs.harvard.edu/abs/2013MNRAS.432.1709C>`_
of the Adaptive Metropolis algorithm by
`Haario H., Saksman E., Tamminen J. (2001) <https://doi.org/10.2307/3318737>`_
for Bayesian analysis.

Usage Example
-------------
To learn how to use ``AdaMet`` run the example program in the
``adamet/examples`` directory, within the main package installation
folder inside ``site-packages``, and read the detailed documentation
in the docstring of the file ``adamet.py`` or on
`PyPi <https://pypi.org/project/adamet/>`_.

Note: For dimensions = 1 to 6, the optimal acceptance rates are
`rate = [0.441, 0.352, 0.316, 0.279, 0.275, 0.266]`
and the asymptotic value for many parameters is 23%

Calling Sequence
----------------
.. code-block:: python

    pars, lnprob = adamet(lnprob_fun, pars, sigpars, bounds, nstep, 
        fignum=None, inline=True, labels=None, labels_scaling=1, 
        nprint=100, plot=True, quiet=False, seed=None, args=(), kwargs={}):

Parameters
----------
lnprob_fun: callable
    This function returns the natural logarithm of the conditional
    probability of the model, given the data. The probability does not need
    to be normalized as only relative probabilities matter. For this
    reason, inclusion of an arbitrary additive constant in  ``lnprob_fun``
    leaves the output of ``AdaMet`` unaffected::

        P(model | data) ~ P(data | model)*P(model)

pars0: array_like, shape (n,)
    vector with the mean of the multivariate Gaussian describing the
    proposal distribution from which samples are drawn.
    For maximum efficiency, this initial Gaussian should approximate the
    posterior distribution. This suggests adopting as ``pars0`` an initial
    guess for the model best-fitting parameters.
sigpars0: array_like, shape (n,)
    vector with the dispersion ``sigma`` of the multivariate Gaussian
    describing the proposal distribution.
    For maximum efficiency, this initial Gaussian should approximate the
    posterior distribution. This suggests adopting as ``sigpars`` an
    initial guess of the uncertainty in the model parameters ``pars``.
bounds: 2-tuple of array_like
    Lower and upper bounds on independent variables. Each array must match
    the size of ``pars``. The model probability is set to zero outside the
    bounds. This keyword is also used to define the plotting ranges.
nsteps: integer
    Number of attempted moves in the chain. Typical numbers are a few
    thousands ``nsteps``.

Other Parameters
----------------
fignum: integer, optional
    Specifies the figure number for the plot.
inline: boolean, optional
    Specifies whether to show a plot of the results inline or in a window.
labels: array_like, shape (n,)
    String labels for each parameter to be used in the ``corner_plot``
labels_scaling: float
    Relative scaling for the plotting labels.
nprint: integer
    Specifies the frequency for the intermediate plots, in moves.
    A typical value could be ``nstep/10``.
plot: boolean, optional
    Specifies whether to show a plot of the results or not.
quiet: boolean, optional
    Suppress the printing of the progress of the algorithm.
seed: integer
    Seed for the random generator. Specify this value for a repeatable
    random sequence.
args, kwargs: tuple and dict, optional
    Additional arguments passed to ``lnprob_fun``. Both empty by default.
    The calling signature is ``lnprob_fun(x, *args, **kwargs)``.

Returns
-------
pars: array_like, shape (nsteps, n)
    Posterior distribution for the model parameters
lnprob: array_like, shape (nsteps, n)
    Logarithm of the probbaility of the model, given the data, for each set
    of parameters in the posterior distribution ``pars``.


License
=======

Other/Proprietary License

Copyright (c) 2012-2025 Michele Cappellari

This software is provided as is with no warranty. You may use it for
non-commercial purposes and modify it for personal or internal use, as long
as you include this copyright and disclaimer in all copies. You may not
redistribute the code.