[ABE-L] COLMEA - Colóquio Interinstitucional Modelos Estocásticos e Aplicações - UFRJ -15/04
eulalia em cbpf.br
eulalia em cbpf.br
Qui Abr 2 18:02:47 -03 2015
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.
----- End forwarded message -----
Mais detalhes sobre a lista de discussão abe