Difference between revisions of "SOCR News IASE Distributome 2021"

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* '''Talk Title''':  ''Probability Distributome – Computing, Visualization, and Instruction''
 
* '''Talk Title''':  ''Probability Distributome – Computing, Visualization, and Instruction''
 
* '''Paper Authors''':  Jared Tianyi Chai, Mark Bobrovnikov, and [https://umich.edu/~dinov Ivo Dinov], [https://www.socr.umich.edu SOCR, University of Michigan].
 
* '''Paper Authors''':  Jared Tianyi Chai, Mark Bobrovnikov, and [https://umich.edu/~dinov Ivo Dinov], [https://www.socr.umich.edu SOCR, University of Michigan].
* '''Other Presenters in the F3-F4 session''': Claudia C. Sutter, Hamid R. Sanei, Courtney Donovan.
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* '''Handout''': [https://wiki.socr.umich.edu/images/7/73/IASE_ISI_WSC_2021_DIstributome_2021_Notes.pdf PDF Handout]
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* Other Presenters in the F3-F4 session: Claudia C. Sutter, Hamid R. Sanei, Courtney Donovan.
  
 
==Abstract==  
 
==Abstract==  
: The concept of a probability distribution is in the core of all modern data-driven modeling, simulation, analytics, inference, prediction, and prognostication of observable natural phenomena. Despite the large number of possible probability distributions and their significant heterogeneity, dozens of distributions are named and well understood, many are interconnected, and all of them span the universe of possible physical processes. This manuscript reports on the probability Distributome, which allows navigation, exploration and learning of many univariate distributions, and facilitate the computation and visualization associated with univariate, bivariate and trivariate probability distributions. We illustrate applications of three specific resources including an HTML5/JavaScript applications ([http://Distributome.org Distributome]), [https://socr.umich.edu/html/dist RShiny apps] and [https://socr.umich.edu/HTML5/BivariateNormal2D and 3D plotly apps]. These resources are freely accessible, open-source, and platform agnostic. Both learners and instructors may utilize, expand, improve, and customize these tools for their specific research and education needs.
+
: The concept of a probability distribution is in the core of all modern data-driven modeling, simulation, analytics, inference, prediction, and prognostication of observable natural phenomena. Despite the large number of possible probability distributions and their significant heterogeneity, dozens of distributions are named and well understood, many are interconnected, and all of them span the universe of possible physical processes. This manuscript reports on the probability Distributome, which allows navigation, exploration and learning of many univariate distributions, and facilitate the computation and visualization associated with univariate, bivariate and trivariate probability distributions. We illustrate applications of three specific resources including an HTML5/JavaScript applications ([http://Distributome.org Distributome]), [https://socr.umich.edu/html/dist RShiny apps] and [https://socr.umich.edu/HTML5/BivariateNormal/BVN2/ and 3D plotly apps]. These resources are freely accessible, open-source, and platform agnostic. Both learners and instructors may utilize, expand, improve, and customize these tools for their specific research and education needs.
 
 
  
 
==Background==
 
==Background==

Latest revision as of 09:22, 26 August 2021

SOCR News & Events: Probability Distributome – Computing, Visualization, and Instruction

Logistics

Abstract

The concept of a probability distribution is in the core of all modern data-driven modeling, simulation, analytics, inference, prediction, and prognostication of observable natural phenomena. Despite the large number of possible probability distributions and their significant heterogeneity, dozens of distributions are named and well understood, many are interconnected, and all of them span the universe of possible physical processes. This manuscript reports on the probability Distributome, which allows navigation, exploration and learning of many univariate distributions, and facilitate the computation and visualization associated with univariate, bivariate and trivariate probability distributions. We illustrate applications of three specific resources including an HTML5/JavaScript applications (Distributome), RShiny apps and and 3D plotly apps. These resources are freely accessible, open-source, and platform agnostic. Both learners and instructors may utilize, expand, improve, and customize these tools for their specific research and education needs.

Background


References




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