Difference between revisions of "SOCR News IASE Distributome 2021"

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(Logistics)
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==Logistics==
 
==Logistics==
 
* [https://iase-web.org/conference/satellite21/?themes 2021 IASE Conference on Statistics Education in the Era of Data Science] (Distance/virtual event)
 
* [https://iase-web.org/conference/satellite21/?themes 2021 IASE Conference on Statistics Education in the Era of Data Science] (Distance/virtual event)
* '''Dates/Times''': August 30 – September 4, 2021 (Time TBD)
+
* '''Dates/Times''': August 30 – September 4, 2021
 +
* Specific Time: 9:30 AM [https://time.is/CEST CEST] (3:30 AM [https://time.is/EDT US EDT])
 
* Registration: [https://iase-web.org/conference/satellite21/?registration Participants have to register to attend in advance] (Per ISI/IASE registration for students is free)
 
* Registration: [https://iase-web.org/conference/satellite21/?registration Participants have to register to attend in advance] (Per ISI/IASE registration for students is free)
* '''Program''':  [https://iase-web.org/conference/satellite21/?programme Program]
+
* '''Program''':  [https://iase-web.org/conference/satellite21/?programme Program] and [https://whova.com/embedded/session/ipcco_202108/1862659/?view= Wed 9/1/21 Session G2-3 (Statistical Learning from the COVID-19 Pandemic & Promoting Data Literacy and Statistical Literacy)]
 
* '''Title''':  ''Probability Distributome – Computing, Visualization, and Instruction''
 
* '''Title''':  ''Probability Distributome – Computing, Visualization, and Instruction''
 
* '''Authors''':  Jared Tianyi Chai, Mark Bobrovnikov, and [https://umich.edu/~dinov Ivo Dinov], [https://www.socr.umich.edu SOCR, University of Michigan].
 
* '''Authors''':  Jared Tianyi Chai, Mark Bobrovnikov, and [https://umich.edu/~dinov Ivo Dinov], [https://www.socr.umich.edu SOCR, University of Michigan].

Revision as of 12:53, 16 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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