SDS 581: Bayesian, Frequentist, and Fiducial Statistics in Data Science — with Xiao-Li Meng
Автор: Super Data Science: ML & AI Podcast with Jon Krohn
Загружено: 2022-06-07
Просмотров: 1440
#BayesianStatistics #FiducialStatistics #FrequentistStatistics
In this episode founding Editor-in-Chief of the Harvard Data Science Review and Professor of Statistics at Harvard University, Prof. Xiao-Li Meng, joins Jon Krohn to dive into data trade-offs that abound, and shares his view on the paradoxical downside of having lots of data.
In this episode you will learn:
• What the Harvard Data Science Review is and why Xiao-Li founded it [3:39]
• The difference between data science and statistics [16:05]
• The concept of 'data minding' [20:36]
• The concept of 'data confession' [28:40]
• Why there’s no “free lunch” with data, and the tricky trade-offs that abound [33:28]
• The surprising paradoxical downside of having lots of data [41:32]
• What the Bayesian, Frequentist, and Fiducial schools of statistics are, and when each of them is most useful in data science [53:56]
Additional materials: https://www.superdatascience.com/581
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