Bayesian inference /
This video is an introduction to using Bayesian Inference, a very important technique in probability and statistics that gives a mechanism for us to update the probabilities that we have for events when new information is provided. For instance, if you test positive for a rare disease with an imperf...
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| Online Access: |
Full text (MCPHS users only) |
|---|---|
| Format: | Electronic Video |
| Language: | English |
| Published: |
Dordrecht, South Holland :
Springer Nature,
2022
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| Series: | Academic Video Online
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| Subjects: |
MARC
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| 520 | |a This video is an introduction to using Bayesian Inference, a very important technique in probability and statistics that gives a mechanism for us to update the probabilities that we have for events when new information is provided. For instance, if you test positive for a rare disease with an imperfect diagnostic test, how likely is it that you have the disease? Bayes' Theorem gives us a tool to answer that type of problem. The video will introduce the major concepts, discuss the prerequisite concept of conditional probability, derive Bayes' theorem, and see a number of examples and complications that will allow students to apply Bayes' theorem in the context of their own disciplines. While Bayesian Inference can be found in many introductory textbooks on probability and statistics, the goal of this specific video is to create a short, standalone introduction so students can start applying the ideas in their own fields immediately. By focusing on developing intuition for the main ideas, students will leave with a changed perspective on how they can view the world probabilistically. Clear explanations, concrete examples, and high production quality involving greenscreens and animations will create a very high level of engagement with this content. The potential audience is broad, as Bayesian inference can apply in a wide range of STEM and non-STEM fields. It will be delivered requiring only minimal mathematical prerequisites and thus accessibly by undergraduate students but would be a topic of interest to many graduate students or professionals who have not had extensive training in probability and statistics. | ||
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