prism 1.1.1

Model dispersion with PRISM; an alternative to MCMC for rapid analysis of models

PRISM is a pure Python 3 package that provides an alternative method to MCMC for analyzing scientific models.

Introduction

Rapid technological advancements allow for both computational resources and observational/experimental instruments to become better, faster and more precise with every passing year. This leads to an ever-increasing amount of scientific data being available and more research questions being raised. As a result, scientific models that attempt to address these questions are becoming more abundant, and are pushing the available resources to the limit as these models incorporate more complex science and more closely resemble reality.

However, as the number of available models increases, they also tend to become more distinct, making it difficult to keep track of their individual qualities. A full analysis of every model would be required in order to recognize these qualities. It is common to employ Markov chain Monte Carlo (MCMC) methods and Bayesian statistics for performing this task. However, as these methods are meant to be used for making approximations of the posterior probability distribution function, there must be a more efficient way of analyzing them.

PRISM tries to tackle this problem by using the Bayes linear approach, the emulation technique and history matching to construct an approximation (‘emulator’) of any given model. The use of these techniques can be seen as special cases of Bayesian statistics, where limited model evaluations are combined with advanced regression techniques, covariances and probability calculations. PRISM is designed to easily facilitate and enhance existing MCMC methods by restricting plausible regions and exploring parameter space efficiently. However, PRISM can additionally be used as a standalone alternative to MCMC for model analysis, providing insight into the behavior of complex scientific models. With PRISM, the time spent on evaluating a model is minimized, providing developers with an advanced model analysis for a fraction of the time required by more traditional methods.

Getting started

$ git clone https://github.com/1313e/PRISM
$ cd PRISM
$ pip install .

$ pip install prism

$ git clone https://github.com/1313e/PRISM
$ cd PRISM
$ pip install -r requirements_dev.txt
$ pytest

$ cd <path_to_installation_directory>/prism
$ pytest

# Imports
from prism import Pipeline
from prism.modellink import GaussianLink

# Define model data and create ModelLink object
model_data = {3: [3.0, 0.1], 5: [5.0, 0.1], 7: [3.0, 0.1]}
modellink_obj = GaussianLink(model_data=model_data)

# Create Pipeline object
pipe = Pipeline(modellink_obj)

# Construct first iteration of the emulator
pipe.construct()

# Create projections
pipe.project()

Community guidelines

PRISM is an open-source and free-to-use software package (and it always will be), provided under the BSD-3 license.

Users are highly encouraged to make contributions to the package or request new features by opening a GitHub issue. If you would like to contribute to the package, but do not know what, then there are quite a few ToDos in the code that may give you some inspiration. As with contributions, if you find a problem or issue with PRISM, please do not hesitate to open a GitHub issue about it.

And, finally, if you use PRISM as part of your workflow in a scientific publication, please consider including an acknowledgement like “Parts of the results in this work were derived using the PRISM Python package.” and citing the PRISM pipeline paper:

@ARTICLE{2019arXiv190108725V,
   author = {{van der Velden}, E. and {Duffy}, A.~R. and {Croton}, D. and
    {Mutch}, S.~J. and {Sinha}, M.},
    title = "{Model dispersion with PRISM; an alternative to MCMC for rapid analysis of models}",
  journal = {arXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1901.08725},
 primaryClass = "astro-ph.IM",
 keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Computational Physics},
     year = 2019,
    month = jan,
   adsurl = {http://adsabs.harvard.edu/abs/2019arXiv190108725V},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}