[ABE-L] COLMEA - Colóquio Interinstitucional Modelos Estocásticos e Aplicações - UFRJ -15/04

[eulalia em cbpf.br]

Prezados colegas,

Escrevo para lembrar que nesta quarta-feira, dia 15, teremos o COLMEA -
Colóquio Interinstitucional Modelos Estocásticos e Aplicações.
Por favor, avisem os estudantes de pós-graduação.
Todos são muito bem-vindos. Detalhes abaixo.
Saudações,

Eulalia

----- Mensagem encaminhada de eulalia em cbpf.br -----
    Data: Thu, 02 Apr 2015 18:02:47 -0300
      De: eulalia em cbpf.br
Assunto: [ABE-L] COLMEA - Colóquio Interinstitucional Modelos  
Estocásticos e Aplicações - UFRJ -15/04
    Para: abe-l em ime.usp.br

Prezados colegas,

no próximo dia 15 de abril teremos, no Instituto de Matemática da UFRJ,
mais um encontro no âmbito do COLMEA - Colóquio Interinstitucional Modelos
Estocásticos e Aplicações.


Programa:

14:00 - 15:20h: Flávio B. Gonçalves (UFMG)

``An infinite-dimensional MCMC for
exact Bayesian inference in jump-diffusion processes"


15:40 - 17:00h: Maria D. Vibranovski (USP)

``The use of genomic and gene expression
large-scale data for the analyses of sexual evolution"


17:00 h: Discussão e lanche


Local: Instituto de Matemática - UFRJ
        Sala C116 - Bloco C do CT - Cidade Universitária -
        Ilha do Fundão


Um cartaz para divulgação encontra-se aqui:

http://www.im.ufrj.br/~coloquiomea/cartaz/2015_04.pdf

Informações mais completas sobre o COLMEA podem ser encontradas aqui:

http://www.im.ufrj.br/~coloquiomea/

Todos são muito bem vindos. Agradecemos também pela divulgação em sua
instituição.

Atenciosamente,

o comitê organizador:  Augusto Q. Teixeira (IMPA), Evaldo M.F. Curado (CBPF),
Fábio D. A. Aarão Reis (UFF),  Maria Eulalia Vares (UFRJ), Mariane Branco
Alves (UFRJ), Patrícia Gonçalves (PUC-Rio)


==========

Resumos das palestras

An infinite-dimensional MCMC for exact Bayesian inference in  
jump-diffusion  processes
Flávio B. Gonçalves (UFMG)


Jump-diffusions have considerable appeal as flexible families of  
stochastic models. Making statistical inference based on discrete  
observations of such processes is a complex and challenging problem.  
Its infinite-dimensional nature has required from existing inference  
methodologies the use of discrete approximations that naturally  
represent a considerable source of error. In this talk, we rely on a  
novel algorithm to perform exact simulation of jump-diffusions bridges  
as the basis to develop an MCMC algorithm to make inference for  
jump-diffusion processes. The resulting infinite-dimensional Markov  
chain has the exact posterior distribution of the parameters and  
missing paths as its invariant distribution. More specifically, it is  
a Gibbs Sampling with Barker's steps. The methodology is exact in the  
sense that it is free of discretisation error and Monte Carlo error is  
the only source of inaccuracy. The exactness feature is related to the  
simulation of events of unknown probability. A simulated example is  
presented to illustrate the methodology.



The use of genomic and gene expression large-scale data for the  
analyses of sexual evolution
Maria D. Vibranovski (USP)


Although more than a decade has passed since the first eukaryotic  
genome was sequenced, the molecular basis of genome organization and  
complexity remains a largely unresolved problem. The relationship of  
genotype to phenotype has proven particularly challenging. I use  
gametogenesis in Drosophila as a model system to study the evolution  
and phenotypic expression of genomic features. Gametogenesis is a  
fascinating biological process; it varies temporally throughout  
development, and has profound evolutionary impact in that it provides
the raw material for the next generation - the gamete. To date,  
gametogenesis research has primarily focused on single gene studies of  
fertility. In contrast, I apply a genomic perspective to the overall  
process of gametogenesis to understand the role sexual selection plays  
in genome evolution. In my research on genome evolution in Drosophila  
melanogaster, I have combined bioinformatics and statistics with  
experimental genomic and molecular genetic methods to obtain  
large-scale gene expression data on gametogenesis, or  
spermatogenic-stage-specific transcriptome (SpermPress). The results  
help to solve two classical problems that have puzzled biologists for  
decades: evidence for Meiotic Sex Chromosome inactivation and for  
Post-meiotic transcription. In this talk, I present the results  
obtained through the application of advanced Bayesian statistics to  
Gene Chip microarray data. I also introduce another puzzle yet to be  
solved in the evolutionary biology field related to the role of sperm  
haploid selection in the evolution of new genes. The discussion of  
alternative analyses and models on spermatogenic transcriptome and  
gene age is pressing and represents part of my current research agenda.

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