“Tutorial on Bayesian Logical Data Analysis”
P. C. Gregory
Department of Physics & Astronomy
Abstract: Increasingly, researchers in many branches of science are coming into contact with the term Bayesian statistics or Bayesian probability theory. The reason why this topic is such a significant leap forward is the subject of a new book:
"Bayesian Logical Data Analysis for the Physical Sciences:
A Comparative Approach with Mathematica Support,"
by P. C. Gregory,
Bayesian probability is a mathematical theory that encompasses both inductive and deductive logic. It provides a conceptually simple and unified approach to all data analysis problems that allows the experimenter to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. Bayesian analysis can frequently yield many orders of magnitude improvement in model parameter estimates by incorporating relevant prior information, and it provides a more powerful way of assessing competing theories at the forefront of science.
The author will provide an introduction to the topic and illustrate the advantages of the Bayesian approach with examples from his new book.