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adamet-2.0.9
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مشاهده بیشترتوضیحات
AdaMet: Adaptive Metropolis for Bayesian Analysis
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | adamet-2.0.9 |
| نام | adamet |
| نسخه کتابخانه | 2.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Michele Cappellari |
| ایمیل نویسنده | michele.cappellari@physics.ox.ac.uk |
| آدرس صفحه اصلی | http://purl.org/cappellari/software |
| آدرس اینترنتی | https://pypi.org/project/adamet/ |
| مجوز | Other/Proprietary License |
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 a number of published papers in the astrophysics literature.
.. contents:: :depth: 1
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 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, pars0, sigpars0, bounds, nstep,
labels=None, nprint=100, quiet=False, fignum=None, plot=True,
labels_scaling=1, seed=None, args=(), kwargs={})
Input Parameters
----------------
lnprob_fun: callable
This function returns the natural logarithm of the conditional
probability of the model, given the data::
P(model | data) ~ P(data | model) P(model)
pars0: array_like with 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 with 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`.
Optional Keywords
-----------------
labels: array_like with shape (n,)
String labels for each parameter to be used in the `corner_plot`
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.
fignum: integer, optional
Specifies the figure number for the plot.
labels_scaling: float
Relative scaling for the plotting labels.
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)`.
Output Parameters
-----------------
pars: array_like with shape (nsteps, n)
Posterior distribution for the model parameters
lnprob: array_like with 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) 2012-2020 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.
نحوه نصب
نصب پکیج whl adamet-2.0.9:
pip install adamet-2.0.9.whl
نصب پکیج tar.gz adamet-2.0.9:
pip install adamet-2.0.9.tar.gz