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Description:
pybalonor 1.0.0
pybalonor
This Python package helps to perform a Bayesian analysis of log-normally
distributed data
(PYthon package for Bayesian Analysis of the LOg-NORmal distribution).
Performing a Bayesian analysis of log-normally distributed data requires
care in the prior choice to yield posterior predictive distributions with
finite moments (e.g. Fabrizi & Trivisano, 2012).
This package uses a simple uniform prior for the log-location and log-variance
parameter. The problem of normalizing the posterior of the mean is solved by
imposing a finite upper bound on the log-variance parameter.
Preface
If you are looking for an analysis of the log-normal distribution, you might
likely want to check out the R package
BayesLN by
Gardini, Fabrizi, and Trivisano. Their conjugate prior is more sophisticated
than the flat prior of pybalonor, and, from limited analysis, seems to lead to
tighter posterior bounds.
If instead you are looking for an analysis based on a flat prior, looking for a
Python solution, or working with a large data set, go ahead!
Installation and Requirements
The following software is required to install pybalonor:
A modern C++ compiler
Boost Math (v1.80.0 or later recommended for numerical stability)
The Meson build system
Cython
NumPy
Mebuex
The Python package can be built from the repository's root directory using
the setuptools build system. For instance, you may call the following command
from the repository's root directory:
pip install --user .
Usage
Currently, pybalonor provides one class, CyLogNormalPosterior:
class CyLogNormalPosterior:
def __init__(self, X, l0_min, l0_max, l1_min, l1_max):
pass
def log_posterior(self, l0, l1):
pass
def log_posterior_predictive(self, x):
pass
def posterior_predictive(self, x):
pass
def posterior_predictive_cdf(self, x):
pass
def log_mean_posterior(self, mu):
pass
The parameters are as follows:
Parameter
Type
Purpose
X
dbuf1
The data set.
x
dbuf1
Where to evaluate the posterior predictive (same dimension as X).
mu
dbuf1
Log-Normal distribution mean (evaluated as density over the posterior)
l0
dbuf1
Log-location parameter l0 at which to evaluate the posterior.
l1
dbuf1
Log-variance parameter l1 (like l0)
l0_min
float
Minimum of log-location parameter for prior.
l0_max
float
Maximum of log-location parameter.
l1_min
float
Minimum of log-variance parameter for prior.
l1_max
float
Maximum of log-variance parameter.
Note: dbuf1 refers to a C-contiguous buffer of doubles (e.g. a one-dimensional NumPy array).
For more information, visit the pybalonor documentation.
License
This software is licensed under the European Public License (EUPL) version 1.2
or later (EUPL-1.2). See the LICENSE file in this directory.
Changelog
The format is based on Keep a Changelog,
and this project adheres to Semantic Versioning.
[1.0.0] - 2023-05-04
Added
Initial release.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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