scholarly journals Bayesian Linear Regression: Teaching resources and software

2021 ◽  
Author(s):  
soumya banerjee
Keyword(s):  
Linear Regression ◽  
Data Science ◽  
Bayesian Models ◽  
Source Code ◽  
Sparse Data ◽  
Teaching Resources ◽  
Model Source ◽  
Bayesian Linear Regression

Bayesian models are very important in modern data science. These models can be used to derive estimatesfor noisy and sparse data. This manuscript outlines the basics and derivations of a Bayesian linearregression model. Source code for performing Bayesian linear regression is also provided. I hope thisresource will enable broader understanding of the basics of Bayesian models.

Bayesian Linear Regression Model

2012 ◽  
Author(s):  
Biliana S. Güner ◽  
Svetlozar T. Rachev ◽  
John S. J. Hsu ◽  
Frank J. Fabozzi
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Bayesian Linear Regression

Shouldn’t a Model “Know” Its Own ID?: Banks Can Significantly Improve Their Model Discipline by Embedding One Critical Piece of Information

2018 ◽  
Keyword(s):  
Source Code ◽  
Geographic Region ◽  
Financial Industry ◽  
Legal Entity ◽  
Root Cause ◽  
Model Source ◽  
Actual Model ◽  
Incomplete Model ◽  
Embedded Model

The unique IDs that firms assign to all important models typically appear in just three places: model documents, validation documents, and model inventory databases. Where the IDs do not, as a rule, appear is within the actual model source code. Incomplete model inventory information (including usage) is a chronic issue throughout the financial industry. Few firms can accurately answer such vexing questions as how many times each model in inventory was executed during the last year, which models exhibit significant seasonality, which models are used in each geographic region or legal entity, or whether any unvalidated models were used during the last year on any firm computer. This article will demonstrate that a root cause of model usage opacity is, unfortunately, that most models do not actually know who they are. This article will further explain how software-embedded model IDs can be leveraged to increase transparency and address some of the most difficult questions that may be posed about model usage.

scholarly journals Publication of Decision Model Source Code: Attitudes of Health Economics Authors

2019 ◽  
Vol 37 (11) ◽  
pp. 1409-1410 ◽  
Author(s):  
Joanna Emerson ◽  
Rachel Bacon ◽  
Alma Kent ◽  
Peter J. Neumann ◽  
Joshua T. Cohen
Keyword(s):  
Health Economics ◽  
Decision Model ◽  
Source Code ◽  
Model Source

Pygenprop: a Python library for programmatic exploration and comparison of organism genome properties

2019 ◽  
Vol 35 (23) ◽  
pp. 5063-5065
Author(s):  
Lee H Bergstrand ◽  
Josh D Neufeld ◽  
Andrew C Doxey
Keyword(s):  
Data Science ◽  
Source Code ◽  
Open Reading Frames ◽  
Third Party ◽  
Partial Support ◽  
Biochemical Pathways ◽  
Bioinformatics Software ◽  
Using Data ◽  
Genome Analyses ◽  
Reading Frames

Abstract Summary A critical step in comparative genomics is the identification of differences in the presence/absence of encoded biochemical pathways among organisms. Our library, Pygenprop, facilitates these comparisons using data from the Genome Properties database. Pygenprop is written in Python and, unlike existing libraries, it is compatible with a variety of tools in the Python data science ecosystem, such as Jupyter Notebooks for interactive analyses and scikit-learn for machine learning. Pygenprop assigns YES, NO, or PARTIAL support for each property based on InterProScan annotations of open reading frames from an organism’s genome. The library contains classes for representing the Genome Properties database as a whole and methods for detecting differences in property assignments between organisms. As the Genome Properties database grows, we anticipate widespread adoption of Pygenprop for routine genome analyses and integration within third-party bioinformatics software. Availability and implementation Pygenprop is written in Python and is compatible with versions 3.6 or higher. Source code is available under Apache Licence Version 2 at https://github.com/Micromeda/pygenprop. The package can be installed from both PyPi (https://pypi.org/project/pygenprop) and Anaconda (https://anaconda.org/lbergstrand/pygenprop). Documentation is available on Read the Docs (http://pygenprop.rtfd.io/).

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