This article jumps right in with continuous-valued probability models, which are explained in Uncertainty, as is the subject of this article in great detail. This article is only a rough summary. The […]
There Is No “Problem” Of Old Evidence In Bayesian Theory
Update I often do a poor job setting the scene. Today we have the solution to an age-old problem (get it? get it?), a “problem” thought to be a reason not to […]
Clever Statistical Method To Discover Fraud (Or Mistakes)
This is in the I-wish-I-had-thought-of-it category. A simple tool that suggests where fraud or major malfunctions in statistical research might exist. First, a description of the tool; second, a description of the […]
Nature: “‘One-size-fits-all’ threshold for P values under fire.” Good. Shoot Them All Down
Nature magazine reports “‘One-size-fits-all’ threshold for P values under fire: Scientists hit back at a proposal to make it tougher to call findings statistically significant.” Researchers are at odds over when to […]
Free Data Science Class: Predictive Case Study 1, Part VII
Review! This is our last week of theory. Next week the practical side begins in earnest. However much fun that will be, and it will be a jolly time, this is the […]
The Big Bang, Eternal Inflation & Many Worlds
We’re back to our Edge series of ideas scientists wish more people knew about. Today is John C. Mather and the Big Bang. Mather isn’t pleased with popular conceptions. What astronomers actually […]
Every Result Of Unsupervised Learning Is Correct; Or, All Learning Is Supervised
The real point I wish to make is that there is no such thing as unsupervised learning; or, stated another way, Truth exists; or, stated another way, every solution to an unsupervised […]
Machines Can’t Learn (Universals): The Abacus As Brain Part II
Read Our Intellects Are Not Computers: The Abacus As Brain Part I first. Machines can learn, all right. But they can’t learn like us. Machines cannot apprehend universals, ideas of truth and […]
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