No Subject

[brunero@pow2.sta.uniroma1.it]

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CON PREGHIERA DI DIFFUSIONE
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                                 AVVISO SEMINARI


                   Il Prof J.O. Berger, Purdue University, terra',
               presso il Dipartimento di Statistica, Probabilita' 
           e Statistiche Applicate, Universita' di Roma "La Sapienza", 
      Piazza Aldo Moro 5, due seminari, secondo il seguente calendario:
 
 Lunedi 20 maggio ore 11, Aula 30
 
       "On the Choice of Hyperpriors in Normal Hierarchical Models"
 
 Lunedi 27 maggio ore 11, Aula 30
 
       "Recent Developments in  Bayesian Model Selection"
 
 
 Tutti gli invitati sono invitati a partecipare.

					(Brunero Liseo)
                          
                            RIASSUNTI 
 
     On the Choice of Hyperpriors in Normal Hierarchical Models
 
				 Abstract
   Hierarchical modelling is wonderful and here to stay, but we usually 
   "cheat" in choosing the prior distributions for hyperparameters. 
   By "cheating" I mean that we usually choose hyperparameter priors in 
   a casual fashion, often feeling that the choice is not too important. 
   Unfortunately, as the number of hyperparameters grows, the effects 
   of casual choices can multiply, leading to considerably inferio
   r performance. As an extreme but not uncommon example, use of the 
   wrong hyperparameter priors can even lead to impropriety of 
   the posterior. Finding a solution to this problem is, unfortunately, 
   difficult; indeed, it is not even clear how to attack the problem. 
   In this talk we simply give some illustrations of the problem, and 
   some "solutions" in special cases. Among the topics to be discussed 
   along the way are reference priors for covariance matrices, 
   and propriety and admissibility of priors in exchangeable 
   hierarchical normal models.


	     Recent Developments in Bayesian Model Selection
			       
			       Abstract 
   There have been rapid advances in default Bayesian model selection 
   and hypothesis testing in recent years, due to the development of 
   the "intrinsic Bayes factor" and "fractional Bayes factor" approaches.
   This talk will cover some of this recent work, including discussion of 
   the "median intrinsic Bayes factor", an especially simple and almost 
   universally applicable method; discussion of situations in which certain 
   default Bayes factors fail; and simplifications that can result when 
   the models being considered have suitable invariance structures.  




	 On the Choice of Hyperpriors in Normal Hierarchical Models

			     Abstract
   Hierarchical modelling is wonderful and here to stay, but we
usually "cheat" in choosing the prior distributions for hyperparameters.
By "cheating" I mean that we usually choose hyperparameter priors in a
casual fashion, often feeling that the choice is not too important.
 Unfortunately,
as the number of hyperparameters grows, the effects of casual choices can
multiply, leading to considerably inferior performance. As an extreme but
not uncommon example, use of the wrong hyperparameter priors can even lead
to impropriety of the posterior.
	Finding a solution to this problem is, unfortunately, difficult;
indeed, it is not even clear how to attack the problem. In this talk we
simply give some illustrations of the problem, and some "solutions" in 
special cases. Among the topics to be discussed along the way are reference
priors for covariance matrices, and propriety and admissibility of priors in 
exchangeable hierarchical normal models,