Understanding the Differences Between Bayesian and Frequentist ...
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Understanding the Differences Between Bayesian and Frequentist ...
“How much more likely is this treatment to generate the desired outcome than the alternative treatment?”
Eric Siegel • Predictive Analytics
In a nutshell, the way I have understood it, given a specific set of data, the frequentist believes that there is a true, underlying distribution from which said data was generated. The inability to get the exact parameters is a function of finite sample size. The Bayesian, on the other hand, think that we start with some assumption about the param... See more
When are Bayesian methods preferable to Frequentist?
nico kokonas added
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
Kaustubh Sule added
Any Bayesian analysis begins with an initial belief, aka a prior. In our case, the prior was the initial guess we had about the location of the target ball. Then we encounter objective information, which in our case was whether the new ball landed to the left or the right of the target ball. When you combine the two it gives you the improved belief
... See moreAl Pittampalli • Persuadable: How Great Leaders Change Their Minds to Change the World
How often are you able to propose a program that will let you predict the benefit with statistical confidence?
Chris Goward • You Should Test That: Conversion Optimization for More Leads, Sales and Profit or The Art and Science of Optimized Marketing
After several tests, however, the initial probability becomes dominated by the new information, which is known as “swamping the prior”
Scott Hartshorn • Tell Me The Odds: A 15 Page Introduction To Bayes Theorem
Fisher and his contemporaries had no problem with the formula called Bayes’s theorem per se, which is just a simple mathematical identity. Instead, they were worried about how it might be applied. In particular, they took issue with the notion of the Bayesian prior.46 It all seemed too subjective: we have to stipulate, in advance, how likely we thi
... See moreNate Silver • The Signal and the Noise: Why So Many Predictions Fail-but Some Don't
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.
nico kokonas added