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Bayesian estimation, particularly using Markov chain Monte

Browse source 
Carlo (MCMC), is an increasingly relevant approach to
statistical estimation. However, few statistical software
packages implement MCMC samplers, and they are non-trivial
 to code by hand. pymc is a python package that implements
the Metropolis-Hastings algorithm as a python class, and is
extremely flexible and applicable to a large suite of problems.
pymc includes methods for summarizing output, plotting,
goodness-of-fit and convergence diagnostics.

WWW:	http://pypi.python.org/pypi/pymc/

PR:		ports/129567
Submitted by:	Wen Heping <wenheping at gmail.com>
This commit is contained in:
Martin Wilke 2008-12-14 10:37:39 +00:00
parent 14b6ba7a44
commit 8cdb0ad932
Notes: svn2git 2021-03-31 03:12:20 +00:00 
svn path=/head/; revision=224222
5 changed files with 286 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

1
math/Makefile
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@ -403,6 +403,7 @@
SUBDIR += py-numpy
SUBDIR += py-plastex
SUBDIR += py-probstat
SUBDIR += py-pymc
SUBDIR += py-scientific
SUBDIR += py-symeig
SUBDIR += py-sympy

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math/py-pymc/Makefile Normal file
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@ -0,0 +1,26 @@
# New ports collection makefile for: py-pymc
# Date created: 2008-12-10
# Whom: Wen Heping <wenheping@gmail.com>
#
# $FreeBSD$
#
PORTNAME= pymc
PORTVERSION= 2.0r1
CATEGORIES= math python
MASTER_SITES= CHEESESHOP
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
DISTNAME= ${PORTNAME}-2.0rc1
MAINTAINER= wenheping@gmail.com
COMMENT= Markov Chain Monte Carlo Sampling Toolkit
BUILD_DEPENDS= ${PYNUMPY}
RUN_DEPENDS= ${BUILD_DEPENDS}
#LIB_DEPENDS= lapack.4:${PORTSDIR}/math/lapack
USE_PYTHON= yes
USE_PYDISTUTILS= yes
PYDISTUTILS_EGGINFO= ${DISTNAME}-${PYTHON_VERSION:S/thon//}.egg-info
.include <bsd.port.mk>

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math/py-pymc/distinfo Normal file
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@ -0,0 +1,3 @@
MD5 (pymc-2.0rc1.tar.gz) = b8e8bc8e72b23a0215d10e13839738d8
SHA256 (pymc-2.0rc1.tar.gz) = fdb7b27c01509e2293d8905697feee3a157478393683eab3deb46a02f486f70a
SIZE (pymc-2.0rc1.tar.gz) = 978218

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math/py-pymc/pkg-descr Normal file
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@ -0,0 +1,11 @@
Bayesian estimation, particularly using Markov chain Monte
Carlo (MCMC), is an increasingly relevant approach to
statistical estimation. However, few statistical software
packages implement MCMC samplers, and they are non-trivial
to code by hand. pymc is a python package that implements
the Metropolis-Hastings algorithm as a python class, and is
extremely flexible and applicable to a large suite of problems.
pymc includes methods for summarizing output, plotting,
goodness-of-fit and convergence diagnostics.
WWW: http://pypi.python.org/pypi/pymc/

245
math/py-pymc/pkg-plist Normal file
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@ -0,0 +1,245 @@
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@dirrm %%PYTHON_SITELIBDIR%%/pymc/MultiModelInference
@dirrm %%PYTHON_SITELIBDIR%%/pymc/database
@dirrm %%PYTHON_SITELIBDIR%%/pymc/examples
@dirrm %%PYTHON_SITELIBDIR%%/pymc/gp/cov_funs
@dirrm %%PYTHON_SITELIBDIR%%/pymc/gp
@dirrm %%PYTHON_SITELIBDIR%%/pymc/tests
@dirrm %%PYTHON_SITELIBDIR%%/pymc