<div dir="ltr">Prezada(o)s, Colegas, <div dir="ltr"><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><br aria-hidden="true"></div><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Entre os dias 09 e 13 de maio acontecerá na UFRJ o Workshop <a href="http://www.dinamicas.im.ufrj.br/celebra-cwinm/" target="_blank" rel="noopener noreferrer" title="http://www.dinamicas.im.ufrj.br/celebra-cwinm/"> Celebrando Mulheres na Matemática</a> .</div><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><br aria-hidden="true"></div><div class="gmail-x_x_elementToProof gmail-x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">O evento será<i> online</i> e as inscrições podem ser feitas <a href="https://forms.gle/YX1wJg64uVTv6Tw89" target="_blank" rel="noopener noreferrer" title="https://forms.gle/YX1wJg64uVTv6Tw89">aqui</a>.</div><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><br aria-hidden="true"></div><div class="gmail-x_x_elementToProof gmail-x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Na segunda-feira, 09/05, entre 10:15h e 12:15h teremos a Sessão de Estatística do evento, onde teremos as seguintes palestras <u>(resumos seguem após final da mensagem)</u>:</div><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><p style="margin-top:0px;line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"><br aria-hidden="true"></p><ul><li><i><span lang="en-US">Mariane B. Alves (IM-UFRJ, Brasil)</span></i><span lang="en-US"></span></li><li style="display:block"><span lang="en-US">k-parametric dynamic generalized linear models: a sequential approach via information geometry</span></li></ul><p style="margin-top:0px;margin-bottom:0px"></p><p style="margin-top:0px;line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"></p><ul><li><i><span lang="fr-CA">Nancy L. Garcia (IMECC, Unicamp, Brasil)</span></i><span lang="en-US"></span></li><li style="display:block"><span lang="en-US">Bayesian analysis of Brazilian and European written texts</span></li></ul><p style="margin-top:0px;margin-bottom:0px"></p><p style="margin-top:0px;line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"></p><ul><li><i><span lang="en-US">Camila P.E. de Souza (University of Western Ontario, Canadá)</span></i><span lang="en-US"></span></li><li style="display:block"><span lang="en-US">Clustering functional data via variational inference</span><i><span lang="en-US"></span></i></li><li style="display:block"><br aria-hidden="true"><i><span lang="en-US"></span></i></li><li><i><span lang="en-US">Lelys Bravo de Guenni (University of Illinois at Urbana-Champaign, EUA)</span></i><span lang="en-US"></span></li><li style="display:block"><span lang="en-US">Deep Lagged-Wavelet for monthly rainfall forecasting in a tropical region</span></li></ul><p style="margin-top:0px;margin-bottom:0px"></p></div><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><br aria-hidden="true"></div><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Contamos com sua presença!</div><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><br aria-hidden="true"></div><div><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Abraços</div><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Alexandra</div><div class="gmail-x_x_elementToProof" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><div class="gmail-x_elementToProof"><div>
</div>
<div><span style="font-size:10pt;color:rgb(102,102,102)">Alexandra M. Schmidt</span><br>
<span style="font-size:10pt"></span><span style="color:rgb(102,102,102)"></span>
<div><span style="font-size:10pt;color:rgb(102,102,102)">Professor - The University Chair<br>
</span></div>
<div><span style="font-size:10pt;color:rgb(102,102,102)">Program Director of Biostatistics</span></div>
<div><span style="font-size:10pt;color:rgb(102,102,102)">McGill University</span><br>
<span style="font-size:10pt;color:rgb(102,102,102)"></span><b><a href="http://alex-schmidt.research.mcgill.ca/"><span style="font-size:10pt">http://alex-schmidt.research.mcgill.ca/</span></a><br></b></div><div><b><img src="cid:ii_l2lyfqo20" alt="image.jpeg" width="123" height="43"></b><span style="color:rgb(237,92,87)"><b></b></span><b><br></b></div></div><div class="elementToProof"><b><a href="https://isbawebmaster.github.io/ISBA2022/"><span style="font-size:10pt">https://isbawebmaster.github.io/ISBA2022/</span></a></b></div></div><div class="gmail-x_elementToProof"><br aria-hidden="true"></div><div class="gmail-x_elementToProof"><p style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"><i><span lang="en-US">Mariane B. Alves (IM-UFRJ)</span></i></p><p style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"><i><span lang="en-US">k-parametric dynamic generalized linear models: a sequential approach via information geometry</span></i></p><p class="gmail-x_elementToProof" style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%" lang="fr-CA">Dynamic
generalized linear models may be seen as an extension to dynamic linear
models, accommodating non-Gaussian responses, and to generalized linear
models, formally treating serial auto-correlation inherent to responses
in the exponential family, observed through time. The Bayesian
inference scheme does not have analytical solution and there are several
numerical approximating proposals in the literature, many of them
relying on Monte Carlo Markov Chain, with the burden of computational
cost. In this talk, a new approach based on information geometry,
focusing on the k-parametric exponential family, will be presented.
Among others, the proposed method accommodates multinomial responses on
k=d+1 categories and normal responses with dynamic predictive structure
for the mean as well as for the precision parameter. The method
preserves the sequential aspect of the Bayesian inferential procedure,
producing real-time inference and naturally accommodates association
among k predictors. Information geometry concepts such as
Kullback-Leibler divergence and the projection theorem are used in the
development of the method, placing it close to recent approaches of
variational inference. The method is computationally efficient and
flexible to quickly accommodate new patterns and information when
strategically needed, favorably comparing to alternative approaches in
the literature, preserving aspects of monitoring and intervention
analysis, which are usual in sequential analyzes. This is joint work
with Raı́ra Marotta and Helio dos Santos Migon.</p><p class="gmail-x_elementToProof" style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%" lang="fr-CA"><br aria-hidden="true"></p><p style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"><i><span lang="fr-CA">Nancy L. Garcia (IMECC, Unicamp, Brazil)</span></i></p><p class="gmail-x_elementToProof" style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"><i><span lang="en-US">Bayesian analysis of Brazilian and European written texts</span></i></p><p class="gmail-x_elementToProof" style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%" lang="en-US">Are
Brazilian and European Modern Portuguese different? Can we identify
these differences through rhythmic characteristics of written texts? We
answer these questions by modelling some characteristics that can be
extracted automatically from written texts as a Markov Chain of Variable
Length. Markov chains with variable length are useful parsimonious
stochastic models able to generate most stationary sequence of discrete
symbols. The idea is to identify the suffixes of the past, called
contexts, that are relevant to predict the future symbol. This is joint
work with Victor Freguglia.</p><p class="gmail-x_elementToProof" style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"><i><span lang="en-US"><br aria-hidden="true"></span></i></p><p class="gmail-x_elementToProof" style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"><i><span lang="en-US">Camila P.E. de Souza (University of Western Ontario)</span></i></p><p style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"><i><span lang="en-US">Clustering functional data via variational inference</span></i></p><p style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%" lang="en-US">Functional
data analysis (FDA) deals with data recorded densely over time (or any
other continuum) with one or more observed curves per subject.
Conceptually, functional data are continuously defined, but in practice,
they are usually observed at discrete points. Among different kinds of
functional data analyses, clustering analysis aims to determine
underlying groups of curves in the data when there is no information on
the group membership of each individual curve. In this work, we propose a
new model-based approach for clustering and smoothing functional data
simultaneously via variational inference (VI). Therefore, we derive a
coordinate ascent variational inference (CAVI) algorithm to approximate
the posterior distribution of our model parameters by finding the
variational distribution with the smallest Kullback-Leibler divergence
to the posterior. To our best knowledge, there are no studies in the
literature on clustering functional data through VI. Our CAVI algorithm
is implemented as an R package, and its performance is evaluated using
simulated data and publicly available datasets. This is joint work with
my students Chengqian Xian and John T. Jewell, and collaborators Ronaldo
Dias and Adriano Zambom.</p><p style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"><br aria-hidden="true"><i><span lang="en-US">Lelys Bravo de Guenni (University of Illinois at Urbana-Champaign, USA)</span></i></p><p style="line-height:108%;text-align:left;margin-bottom:0.28cm;background:transparent none repeat scroll 0% 0%"><span lang="en-US">Deep
Lagged-Wavelet for monthly rainfall forecasting in a tropical region
Rainfall forecasting is an important decision-making input in a variety
of areas, including agriculture, hydropower generation, and water
resource planning and management. A reliable forecasting tool would
contribute to
the reduction of vulnerability and risk in water management systems.
However, due to the high spatial-temporal variability of rainfall
amounts, it is very difficult to achieve high accuracy in the forecasts.
This study addresses the problem of rainfall forecasting by proposing a
methodology based on a combination of wavelet decomposition (WD),
neural networks (NN), and lagged regression (LR). We implemented WD in a
pre-processing phase followed by the use of a recurrent NN algorithm,
and proposed a prediction enhancement phase by optimizing the network
outputs using a monthly rainfall forecast correction with LR. The
methodology was implemented at four weather stations in a tropical
region, and it was compared with other powerful forecasting methods. The
research results suggest that our approach outperformed other methods
in performance accuracy and biases correction, achieving adjusted R
squared greater than 0.76 and normalized mean absolute errors less than
0.31.</span></p></div></div></div></div></div>