Motivated by the statistical evaluation of complex computer models, we deal with the issue of objective prior specification for the parameters of Gaussian processes. In particular, we derive the Jeffreys-rule, independence Jeffreys and reference priors for this situation, and prove that the resulting posterior distributions are proper under a quite general set of conditions. Another prior specification strategy, based on maximum likelihood estimates, is also considered, and all priors are then compared on the grounds of the frequentist properties of the ensuing Bayesian procedures. Computational issues are also addressed in the paper, and we illustrate the proposed solutions by means of an example taken from the field of complex computer model validation.

In the multidimensional framework the robust estimation of the location and the covariance matrix is a highly expensive computational task. A popular estimator is the Minimum Covariance Determinant (MCD; Rousseeuw, 1984, 1985). Different authors proposed approximation algorithms for this estimator. Recently Rousseeuw and van Driessen (1999) seem to stop the competition in providing a fast and good approximation to the MCD with their procedure called FAST-MCD. This algorithm works fine when the spatial configuration of data contains either radial outliers or clusters of outliers having dispersion higher than that of the good points. When the cluster of outlying observations has a dispersion lower than that of the good points the FAST-MCD shows some drawbacks. This behavior highlights some remarks about the robustness of the MCD. In the talk we review the MCD estimator and some algorithms for its approximation, we discuss about the source of failure of the estimator, and we present a new procedure.

Multivariate models are of great importance in theoretical and applied quantitative genetics. We extend quantitative genetic theory to accommodate situations in which there is linear feedback or recursiveness between the phenotypes involved in a multivariate system, assuming an infinitesimal, additive, model of inheritance. It is shown that structural parameters defining a simultaneous or recursive system have a bearing on the interpretation of quantitative genetic parameter estimates (e.g., heritability, offspring-parent regression, genetic correlation) when such features are ignored. Matrix representations are given for treating a plethora of feedback-recursive situations. The likelihood function is derived, assuming multivariate normality, and results from econometric theory for parameter identification are adapted to a quantitative genetic setting. A Bayesian treatment with a Markov chain Monte Carlo implementation is suggested for inference and developed. When the system is fully recursive, all conditional posterior distributions are in closed form, so Gibbs sampling is straightforward. If there is feedback, a Metropolis step is embedded for sampling the structural parameters, since their conditional distributions are unknown. Extensions of the model to discrete random variables and to non-linear relationships between phenotypes are discussed.

Nesta apresentação é feita uma análise de um modelo espaço-temporal, o qual descreve o movimento colectivo de partículas no espaço e no tempo. Considera-se um conjunto $E$ (eventualmente infinito, mas numerável), ao qual chegam partículas de acordo com uma cadeia Markov $(K,X)$. $K$ designa o ambiente aleatório que rege o processo (sendo que $K$ é uma cadeia de Markov homogénea) e $X$ designa o número de partículas que entram no conjunto $E$ em cada instante. Assume-se que o número de partículas geradas em determinado instante depende apenas da transição ocorrida na cadeia $K$, pelo que o processo bivariado $(K,X)$ é uma cadeia Markov modulada.

Uma vez entradas no conjunto $E$, as partículas movem-se ao longo dos elementos de $E$ de forma condicionalmente independente (dada a transição no ambiente e o número de partículas geradas), e de acordo com uma cadeia de Markov absorvente em tempo finito.

Para este sistema é feita uma análise do tipo sample path, apresentando-se nomeadamente leis de médias para diversos funcionais de interesse, nomeadamente:

• Taxa de entrada de partículas num dado subconjunto de $E$;
• Taxa de throughput de $A$ para $B$, onde $A$ e $B$ são subconjuntos (disjuntos) de $E$;
• Taxa de novas visitas a um dado subconjunto.

Finalmente discute-se a validade destes resultados em termos de valor esperado, no quadro de ergodicidade da cadeia moduladora $K$.

Este trabalho é um trabalho conjunto de Nelson Antunes, Cláudia Nunes e António Pacheco.

Brief history of phylogenetic analysis, and some of the related controversies ("cladistics vs. phenetics"). Introduction to terms, principles and methods of phylogenetics, including parsimony, likelihood and distance based approaches. Particular emphasis will be given to the analysis of DNA sequence data. Some of the problems that affect phylogenetic analysis will be discussed, such as homology assessment, horizontal gene transfer (HGT), "gene trees vs. species trees". Information on the most commonly used software will be provided, including a short demonstration on the use of PAUP* (Phylogenetic Analysis Using Parsimony, *and other methods).

The presentation will be in Portuguese (or in English, if requested), with transparencies in English.

We examine a Markov chain model for a random walk on the unit interval. That model is the subject of several problems, starting with 6.4.34, pp. 321 ff. in the book Marcel F. Neuts, "Algorithmic Probability: A Collection of Problems", Chapman and Hall, New York, New York, 1995. The random walk is studied by analytic, numerical and computer-experimental methods. Each of these approaches complement the others. Together, they offer an example of the diverse, hybrid methods that can be brought to bear on the contemporary problems of applied mathematics.

Um dos elementos centrais do sucesso do melhoramento genético é a capacidade de poder prever, com base num modelo genético simples e em métodos estatísticos apropriados, qual o valor genético de um indivíduo. Esta ciência designa-se por Genética Quantitativa. O seu objecto de estudo é poder dizer qual o mérito dos genes que contém (em relação à média da população a que pertence) e que levam a que tenha uma performance melhor. Irei apresentar resumidamente o modelo genético subjacente, os métodos estatísticos mais comuns e os desafios de análise estatística que encontramos na análise de dados reais.

We try to gain insight into the deeper physical behavior of a finite Markov chain by systematically computing quantities related to the visits to a string of nested sets of states. The choice of the successive states added to the nested sets is called an exploratory strategy. The strategy is constructed by focusing of the physical property to be explored. Quantities that serve as criteria in one strategy are reported as descriptors for the other strategies. This is a promising tool for the exploration of finite discrete-time Markov chains. Similar methods can be developed for continuous-time chains and Markov renewal processes, but the required computational methods are substantially different. We believe that this methodology may find applications, among other areas, in genetics and linguistics. The existing Markov chain analysis should be complemented by data analytic procedures applied to real or simulated data bases. The exploration in parallel of the Markov chains and suitable data sets can serve to develop the skills needed to gain reliable insights from the models and from the data sets.

Neste seminário focam-se aspectos gerais sobre o desenvolvimento de uma planta. Em particular é discutida a questão de clonagem de pinheiros feita a partir de árvores melhoradas, de valor florestal comprovado. No seguimento desta questão discute-se como é possível criopreservar embriões de pinheiro por tempo indefinido. Uma vez que o termo chave é transformação genética, neste seminário será discutida o que é, como é efectuada e quais os resultados.

An efficient utilisation of the radio resources in mobile communications is of a great importance. In general a high degree of sharing is efficient, but requires service protection mechanisms to guarantee the Quality of Service for all customers. We study the effect of cell breathing and overlapping along with hierarchical cell structures. We show that by call packing we obtain a high utilisation. The transformation from cell-based network to direct routing network model is used to carry out calculations. The models in discussion are a generalisation of the Erlang-B formula, including general arrival processes and multi-rate (multi-media) traffic for second and third generation systems.

Esta palestra tem um carácter académico mais do que científico. O nosso objectivo é discutir o conceito de informação estatística como nos foi apresentado pelo Professor Dev Basu. Apresentaremos um exemplo simples de bolas em urnas. Com esse exemplo discutiremos o conceito de informação e mostraremos que podemos perder informação quando realizamos novos experimentos. Usaremos o conceito de Informação de DeGroot e suficiência de Blackwell para escolher experimentos. Por último mostraremos como podemos estabelecer o tamanho de amostra mínimo para atingir objectivos bem definidos.