01NUMKK - Bayesian Networks - 2010

People

The course is taught by Mauro Gasparini, Julia Mortera, Alfredo Braunstein e Daniele Imparato between November and December 2010. Classes will take place in Aula Buzano, Department of Mathematics, third floor central right elevator, C.so Duca degli Abruzzi 24, Torino, according to the schedule below.

Dates

08/11/10Gasparini Presentation of the course. Random vectors.
The multinomial distribution.
09/11/10Gasparini The Dirichlet and the multivariate normal distributions.
Their marginal, joint and conditional distributions.
15/11/10Gasparini DAGs to represent multivariate distributions.
Introduction to stochastic simulation.
16/11/10Gasparini Univariate and multivariate simulation techniques.
Independent and Markov Chain Monte Carlo (MCMC) simulation.
22/11/10Gasparini Introduction to Bayesian Statistics.
Exact Bayesian computations in conjugate cases.
23/11/10Gasparini Statistical Bayesian networks.
MCMC for approximate Bayesian computations.
29/11/10 Imparato Variance reduction techniques in MCMC.
30/11/10Gasparini class canceled due to abusive behavior by protesters
against proposals for university reform
06/12/10Gasparini The software WinBUGS.
Statistical Bayesian Networks.
Class material: normal.R and normal.txt .
07/12/10Gasparini Exact Bayesian computations for discrete Bayesian networks.
Introduction to the sum-product and the junction tree algorithms.
13/12/10Mortera Probabilistic Expert Systems for forensic problems.

14/12/10Mortera The software HUGIN with some applications.
20/12/10Luca Carlone Student seminar on Bayesian Networks in Robotics
21/12/10Braunstein Belief propagation for optimization

References

  • Ross S.M. (2006) A first course in probability. Prentice-Hall, 7th ed.
    Italian translation: Ross S.M. (2007) Probabilità e Statistica per l'ingegneria e le scienze. Apogeo, seconda edizione.
  • Mauro Gasparini (2006). Modelli probabilistici e statistici. CLUT. Chapter 2.
  • Ross S.M. (2002) Simulation. Academic Press. In the DIMAT library.
  • Casella G. and George E.I. (1992). Explaining the Gibbs Sampler. The American Statistician, 46, 167-174.
  • Chib S. and Greenberg E. (1995). Understanding the Metropolis-Hastings algorithm. The American Statistician, 49,4, 327-335.
  • Bayesian Statistics on Scholarpedia.
  • Gelman A., Carlin J.B., Stern H.S. and Rubin D.B. (2004). Bayesian Data Analysis. Chapman and Hall. In the DISPEA library.
  • Gilks W.R., Richardson S. and Spiegelhalter D.J., eds., (1996). Markov Chain Monte Carlo in practice. Chapman and Hall.
  • WinBUGS Examples: Volume 1, Volume 2, Volume 3.
  • Lunn D., Spiegelhalter D., Thomas A. and Best N. (2009). The BUGS project: Evolution, critique and future directions. Statistics in Medicine, 28, 3049-3067.
  • Kschischang F.R., Frey B.J. and Loeliger H.-A. (2001). Factor graphs and the sum-product algorithm. IEEE Transactions on information theory, 47, 498-519.
  • Cowell, R.G., Dawid A.P., Lauritzen S.L. and Spiegelhalter, D.J. (1999). Probabilistic networks and expert systems. Springer.
    The original edition is in the DIMAT library. A paperback edition was published in 2007.
  • Introduction to inference in Bayesian networks by R. Cowell. In Jordan M. (ed.) (1998). Learning in graphical models. The MIT press.
  • Advanced inference in Bayesian networks by R. Cowell. In Jordan M. (ed.) (1998). Learning in graphical models. The MIT press.
  • HUGIN

Last update 06/12/10. Back to Mauro Gasparini.