This is the first of a two part series about Bayes Theorem and the Second Law of Thermodynamics. It begins with the question: What are the odds that one hour from now, the diffuse air in the sealed and insulated room I’m in will position itself in th...
Shalizi wrote a paper purporting to show Statistical Mechanics couldn’t be an application of Bayesian Statistics because Bayesian updating implies Entropy is decreasing. Such confusion arises because empirical entropies computed from frequencies are ...
Criticizing P-values and Confidence Intervals (CI’s) is nothing new, but my aim is different. My goal is to tear down, shred, burn and destroy them using arguments you probably haven’t seen before, aided by Entropy and it’s various ma...
Statistics jumped the shark the moment they adopted the following definition, (Gelman & Hill, page 13): A probability distribution corresponds to an urn with a potentially infinite number of balls inside. When a ball is drawn at random, the “...
The post “What do we need to Model?” showed what our goal in modeling errors should be. This one shows how it’s achieved. Assigning a distribution to the fixed parameters is like finding a prior ; it’s successful whenever the &#...
Part of the communication difficulty between Bayesians and Frequentists is that they’re modeling different things using similar mathematics. So it’s worth looking closely at a simple example to see what each is hoping to achieve with their ...
Most Statisticians think of sampling distributions as a kind of physical model for an infinite sequence of events. They view priors on the other hand as something different since they can be assigned to one-off hypothesis like “Republicans will w...
