**6 ^{th} International Mathematica Syposium, **

**“Tutorial on Bayesian Logical Data
Analysis”**

** P. C. Gregory **

** ****http://www.physics.ubc.ca/~gregory/gregory.html**

** 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,

See http://books.cambridge.org/052184150X.htm

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.