Insegnamento a.a. 2006-2007

Go to class group/s: 31

GM-LS (6 credits - I sem. - AI) - MM-LS (6 credits - I sem. - AI) - OSI-LS (6 credits - I sem. - AI) - AFC-LS (6 credits - I sem. - AI) - CLAPI-LS (6 credits - I sem. - AI) - CLEFIN-LS (6 credits - I sem. - AI) - CLELI-LS (6 credits - I sem. - AI) - CLEACC-LS (6 credits - I sem. - AI) - DES-LS (6 credits - I sem. - AI) - CLEMIT-LS (6 credits - I sem. - AI) - CLG-LS (6 credits - I sem. - AI)

Course Director:
PIETRO MULIERE

Course Objectives

The course is intended to be an introduction to Bayesian statistics for students who have already been exposed to a good preliminary course of statistics from a classical point of view.
We also assume that our audience includes those who are interested in using Bayesian methods to model real problems in various scientific disciplines (economics, finance, econometrics, survival analysis, demography, reliability, etc.)
Such students usually want to understand foundational principles well enough so that they feel comfortable using statistical procedures; are able to recommend solutions based upon these procedures to decision makers; are intrigued enough to seek additional background understanding through relevant literature.
For this reason we have tried to maximize interpretation of the theory and have minimized our dependence upon proof of theorems.

Course Content Summary

• Subjective probability
  Definition of coherence; definition of probability; existence of a probability; properties.
• The Bayesian Methods
  Bayes' Theorem; Posterior Distributions; Prior Distributions; Decision Theory.
• Inference
  Predictive and parametric inference; Coniugate Analysis; Parametric point estimation; Test of Hypotheses.
• Exchangeables sequences of random variables
  Exchangeability events; mixtures of i.i.d. random variables and exchangeability; de Finetti's representation theorem; partial exchangeability according to de Finetti; partial exchangeability according to Diaconis and Freedman.
• Predictive sufficient statistics
  Predictive sufficient statistic: definitions and properties; connections between predictive sufficient statistics and statistical models; predictive sufficient statistics and exchangeability; exchangeability and characterizations of the models; predictive justifications of the priors.
• Prior processes for the Bayesian non-parametric
  Constructing random probability measures; the Dirichlet process; polya Trees; neutral to the right priors.
• Urn schemes for constructing priors
  Background on two colors urn processes; a Polya urn for the Dirichlet process; reinforced urn processes; reinforced urn processes for the beta-Stacy prior; reinforced urn processes for the Polya tree; neutral to the prior;
• Linear models
  Linear models; Kalman filter.

Detailed Description of Assessment Methods

The exam  consists of an essay on a topic chosen by the student, that is discussed with the professors.

Textbooks

• M.J. BERNARDO , A.F.M. SMITH, Bayesian Theory, J. Wiley, 1994.

Exam textbooks & Online Articles (check availability at the Library)

Last change 17/05/2006 00:00