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AdaMet: Adaptive Metropolis for Bayesian Analysis

Project description

Adaptive Metropolis for Bayesian Analysis

AdaMet is a well-tested Python implementation of the Adaptive Metropolis algorithm by Haario H., Saksman E., Tamminen J., (2001). It was used in a number of published papers in the astrophysics literature.


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

    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}


install with:

pip install adamet

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

pip install --user adamet


See adamet/examples


Copyright (c) 2012-2018 Michele Cappellari

This software is provided as is without any warranty whatsoever. Permission to use, for non-commercial purposes is granted. Permission to modify for personal or internal use is granted, provided this copyright and disclaimer are included in all copies of the software. All other rights are reserved. In particular, redistribution of the code is not allowed.

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