When are Bayesian methods preferable to Frequentist?
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When are Bayesian methods preferable to Frequentist?
In this respect, Bayesian inference is more intuitive at its core and in closer alignment with our natural mode of probabilistic reasoning than frequentist inference. For example, we are more interested in the probability that 1 treatment is superior to another (Bayesian probability) than in the probability of obtaining certain data assuming the tr... See more
Understanding the Differences Between Bayesian and Frequentist ...
The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy
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Essentially, the frequentist approach toward statistics seeks to wash its hands of the reason that predictions most often go wrong: human error. It views uncertainty as something intrinsic to the experiment rather than something intrinsic to our ability to understand the real world. The frequentist method also implies that, as you collect more data
... See moreNate Silver • The Signal and the Noise: Why So Many Predictions Fail-but Some Don't
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
Bayesians are concerned above all with uncertainty. All learned knowledge is uncertain, and learning itself is a form of uncertain inference. The problem then becomes how to deal with noisy, incomplete, and even contradictory information without falling apart. The solution is probabilistic inference, and the
Pedro Domingos • The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
The core of Bayesian thinking (or Bayesian updating, as it can be called) is this: given that we have limited but useful information about the world, and are constantly encountering new information, we should probably take into account what we already know when we learn something new.
Rhiannon Beaubien • The Great Mental Models Volume 1: General Thinking Concepts
The transparency of Bayesian networks distinguishes them from most other approaches to machine learning, which tend to produce inscrutable “black boxes.” In a Bayesian network you can follow every step and understand how and why each piece of evidence changed the network’s beliefs.
Dana Mackenzie • The Book of Why: The New Science of Cause and Effect
In many walks of life, expressions of uncertainty are mistaken for admissions of weakness. When you first start to make these probability estimates, they may be quite poor. But there are two pieces of favorable news. First, these estimates are just a starting point: Bayes’s theorem will have you revise and improve them as you encounter new informat
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