Difference between revisions of "SOCR News IASE Distributome 2021"
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[[Image:SOCR_UMich_2020a.png|150px|thumbnail|right| [https://socr.umich.edu SOCR Resource] ]] | [[Image:SOCR_UMich_2020a.png|150px|thumbnail|right| [https://socr.umich.edu SOCR Resource] ]] | ||
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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 | + | * '''Dates/Times''': August 30 – September 4, 2021. |
| − | * Registration: [https://iase-web.org/conference/satellite21/?registration Participants have to register to attend in advance] (Per ISI/IASE registration for students is free) | + | * '''European CEST Date/Time''': Wednesday, September 1, 2021, [https://time.is/CEST CEST 2:00AM - 3:30AM (early morning)]. |
| − | * '''Program''': [https:// | + | * '''US EDT Date/Time''': Tuesday, August 31, 2021, [https://time.is/EDT EDT 20:00-21:30 (evening)] |
| − | * '''Title''': ''Probability Distributome – Computing, Visualization, and Instruction'' | + | * '''Registration''': [https://iase-web.org/conference/satellite21/?registration Participants have to register to attend in advance] (Per ISI/IASE registration for students is free). |
| − | * '''Authors''': Jared Tianyi Chai, Mark Bobrovnikov, and [https://umich.edu/~dinov Ivo Dinov], [https://www.socr.umich.edu SOCR, University of Michigan]. | + | * '''Program''': [https://whova.com/embedded/session/ipcco_202108/1862654/?view= Sessions F3-F4 (Promoting Data Literacy and Statistical Literacy & Statistics and Data Science for Social Good)] |
| + | * '''Virtual Connection''': [https://whova.com/portal/webapp/ipcco_202108/Agenda Whova/Zoom]. | ||
| + | * '''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]. | ||
| + | * '''Handout''': [https://wiki.socr.umich.edu/images/7/73/IASE_ISI_WSC_2021_DIstributome_2021_Notes.pdf PDF Handout] | ||
| + | * 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/ | + | : 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. |
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==Background== | ==Background== | ||
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==References== | ==References== | ||
| − | * This work is sponsored in part by NIH Grants [http://projectreporter.nih.gov/project_info_details.cfm?aid=8975330&icde=25689118 P30 DK089503], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8821268&icde=22205726 P20 NR015331], [https://projectreporter.nih.gov/project_info_description.cfm?aid=9816658 R01CA233487], | + | * This work is sponsored in part by NIH Grants [http://projectreporter.nih.gov/project_info_details.cfm?aid=8975330&icde=25689118 P30 DK089503], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8821268&icde=22205726 P20 NR015331], [https://projectreporter.nih.gov/project_info_description.cfm?aid=9816658 R01CA233487], [https://projectreporter.nih.gov/project_info_details.cfm?aid=9842024 R01MH121079], and [https://reporter.nih.gov/project-details/10204560 T32GM141746], as well as, NSF Grants [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1916425 1916425], [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1734853 1734853] and [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1636840 1636840]. |
* SOCR Home page: https://www.socr.umich.edu | * SOCR Home page: https://www.socr.umich.edu | ||
{{translate|pageName=https://wiki.socr.umich.edu/index.php/SOCR_News_IASE_Distributome_2021}} | {{translate|pageName=https://wiki.socr.umich.edu/index.php/SOCR_News_IASE_Distributome_2021}} | ||
Latest revision as of 08:22, 26 August 2021
Contents
SOCR News & Events: Probability Distributome – Computing, Visualization, and Instruction
Logistics
- 2021 IASE Conference on Statistics Education in the Era of Data Science (Distance/virtual event).
- Dates/Times: August 30 – September 4, 2021.
- European CEST Date/Time: Wednesday, September 1, 2021, CEST 2:00AM - 3:30AM (early morning).
- US EDT Date/Time: Tuesday, August 31, 2021, EDT 20:00-21:30 (evening)
- Registration: Participants have to register to attend in advance (Per ISI/IASE registration for students is free).
- Program: Sessions F3-F4 (Promoting Data Literacy and Statistical Literacy & Statistics and Data Science for Social Good)
- Virtual Connection: Whova/Zoom.
- Talk Title: Probability Distributome – Computing, Visualization, and Instruction
- Paper Authors: Jared Tianyi Chai, Mark Bobrovnikov, and Ivo Dinov, SOCR, University of Michigan.
- Handout: PDF Handout
- Other Presenters in the F3-F4 session: Claudia C. Sutter, Hamid R. Sanei, Courtney Donovan.
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
- SOCR News & Events
- SOCR Global Users
- SOCR Navigators
- SOCR Datasets and Challenging Case-studies
- Electronic Textbooks:
References
- This work is sponsored in part by NIH Grants P30 DK089503, P20 NR015331, R01CA233487, R01MH121079, and T32GM141746, as well as, NSF Grants 1916425, 1734853 and 1636840.
- SOCR Home page: https://www.socr.umich.edu
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