Understanding the Differences Between Bayesian and Frequentist ...
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Understanding the Differences Between Bayesian and Frequentist ...
The difference is statistically significant, but is it practically significant? The answer to this question depends on how we interpret the lowest and highest plausible differences.
Jeff Sauro • Quantifying the User Experience: Practical Statistics for User Research
Conditional probability is similar to Bayesian thinking in practice, but comes at it from a different angle. When you use historical events to predict the future, you have to be mindful of the conditions that surrounded that event. Events can be independent, like tossing a coin, or dependent. In the latter case, it means the outcomes of an event ar
... See moreRhiannon Beaubien • The Great Mental Models Volume 1: General Thinking Concepts
Bayesian thinking:
Rhiannon Beaubien • The Great Mental Models Volume 1: General Thinking Concepts
But what are the messages? This took me quite a few months to figure out. I finally realized that the messages were conditional probabilities in one direction and likelihood ratios in the other.
Dana Mackenzie • The Book of Why: The New Science of Cause and Effect
finally realized that the messages were conditional probabilities in one direction and likelihood ratios in the other.
Judea Pearl, Dana Mackenzie • The Book of Why
The point of Bayesian decision theory is to help make a decision. Or, more accurately, to describe the optimal way of making a decision, given uncertainty about the outcome.
In a 1981 editorial on using a computer to interpret risk after exercise stress testing, Robert Califf and Robert Rosati wrote, “Proper interpretation and use of computerized data will depend as much on wise doctors as any other source of data in the past.”
Eric Topol • Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
The aim of the research is to determine whether the treated group improves more than regression can explain.