Tea: A High-level Language and Runtime System for Automating Statistical Analysis [Python module]

Channel:
Microsoft Research
Subscribers:
344,000
Published on ● Video Link: https://www.youtube.com/watch?v=eyoAqNKTjGQ


Duration: 38:18
3,703 views
113

Current statistical tools place the burden of valid, reproducible statistical analyses on the user. Users must have deep knowledge of statistics to not only identify their research questions, hypotheses, and domain assumptions but also select valid statistical tests for their hypotheses. As quantitative data become increasingly available in all disciplines, data analysis will continue to become a common task for people who may not have statistical expertise. Tea, a high-level declarative language for automating statistical test selection and execution, abstracts the details of analyses from users, empowering them to perform valid analyses by expressing their goals and domain knowledge. In this talk, I will discuss the design and implementation of Tea, lessons learned through the process, and other ongoing work in this vein.

Talk slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/Tea-A-High-level-Language-and-Runtime-System-for-Automating-Statistical-Analysis-SLIDES.pdf

See more on this and other talks at Microsoft Research: https://www.microsoft.com/en-us/research/video/tea-a-high-level-language-and-runtime-system-for-automating-statistical-analysis/



Other Videos By Microsoft Research


2019-10-14Can quantum mechanics help us learn models of classical systems?
2019-10-14Reinforcement Learning From Small Data in Feature Space
2019-10-14Reward Machines: Structuring Reward Function Specifications and Reducing Sample Complexity...
2019-10-14Safe and Fair Reinforcement Learning
2019-10-14Scalable and Robust Multi-Agent Reinforcement Learning
2019-10-14Structure Visual Understanding and Interaction with Human and Environment
2019-10-14Improving Doctor-Patient Interaction with ML-Enabled Clinical Note Taking
2019-10-11HapSense: A Soft Haptic I/O Device with Uninterrupted Dual Functionalities...
2019-10-09Advanced polarized light microscopy for mapping molecular orientation
2019-10-09Data science and ML for human well-being with Jina Suh [Podcast]
2019-10-07Tea: A High-level Language and Runtime System for Automating Statistical Analysis [Python module]
2019-10-07Discover[i]: Component-based Parameterized Reasoning for Distributed Applications
2019-10-04Scheduling For Efficient Large-Scale Machine Learning Training
2019-10-03Distributed Entity Resolution for Computational Social Science
2019-10-03MMLSpark: empowering AI for Good with Mark Hamilton [Podcast]
2019-10-02Non-linear Invariants for Control-Command Systems
2019-10-02Vision-and-Dialog Navigation
2019-10-01The Future of Mathematics?
2019-09-30How Not to Prove Your Election Outcome
2019-09-30The Worst Form Including All Those Others: Canada’s Experiments with Online Voting
2019-09-30DIFF: A Relational Interface for Large-Scale Data Explanation


Tags:
Python module
Statistical Analysis
statistical tools
statistical tests
quantitative data
data analysis
Runtime System
Automating Statistical Analysis
Python
Eunice Jun
Microsoft Research
MSR