[Forum SIS] avviso di seminario Prof. P. J. Green

[Julia Mortera]

*
*************************AVVISO di SEMINARIO del Prof. Peter 
GREEN***********************
*
Nell'ambito del dottorato in Metodi Statistici per l'Economia e 
l'Impresa della Scuola dottorale in Economia e Metodi 
Quantitativi                                                                                         
presso il Dipartimento di Economia, Università degli Studi Roma Tre, Via 
Silvio D'Amico 77, 00145 Roma

*mercoledì 19 ottobre 2011 alle ore 16:30,  aula 24 - 3° piano *

*Prof. Peter Green
Department of Mathematics
University of Bristol, UK
*

terrà il seminario

*Bayesian graphical model determination *

*Abstract*
The structure in a multivariate distribution is largely captured by the 
conditional independence relationships that hold among the variables, 
often represented graphically, and inferring these from data is an 
important step in understanding a complex stochastic system. 
Simultaneous inference about the conditional independence graph and 
parameters of the model is known as joint structural and quantitative 
learning in the machine learning literature: it is appealing to conduct 
this in Bayesian paradigm, but this can pose computational challenges, 
because of the huge size of the model space that is involved, unless 
there are very few variables. After setting the scene, I will present 
some recent joint work with Alun Thomas (Utah), that exploits new 
results on perturbations to graphs that maintain decomposability and on 
enumeration of junction trees to construct a Markov chain sampler on 
junction trees that can be used to compute joint inference about 
structure and parameters in graphical
models on quite a large scale.

  ~ ~ ~

*mercoledì 26 ottobre 2011 ore 16:30  aula 24 - 3° piano *
*Prof. Peter Green
*

terrà il seminario
*
*
*Identifying influential model choices in Bayesian hierarchical models *

*Abstract*

Real-world phenomena are frequently modelled by Bayesian hierarchical 
models. The building blocks in such models are the distributions of each 
variable conditional on parent and/or neighbour variables in the graph. 
The specifications of centre and spread of these conditional 
distributions may be well-motivated, while the tail specifications are 
often left to convenience. However, the posterior distribution of a 
parameter may depend strongly on such arbitrary tail specifications. 
This is not easily detected in complex models.

In this paper we propose a graphical diagnostic which identifies such 
influential statistical modelling choices at the node level in any chain 
graph model.  Our diagnostic, the local critique plot, examines local 
conflict between the information coming from the parents and neighbours 
(local prior) and from the children and co-parents (lifted likelihood). 
It identifies properties of the local prior and the lifted likelihood 
that are influential on the posterior density. We illustrate the use of 
the local critique plot with applications involving models of different 
levels of complexity. The local critique plot can be derived for all 
parameters in a chain graph model, and is easy to
implement using the output of posterior sampling

~ ~ ~
*Tutti gli interessati sono invitati a partecipare*

-- 
Julia Mortera
Dipartimento di Economia
Università Roma Tre
Via Silvio D'Amico 77
00145 Roma
Tel: +39 06 57335732
Fax: +39 06 57335771
URL:  http:http://dipeco.uniroma3.it/docenti.asp?id=123&nomedocente=Julia%20Mortera


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