Welcome

The Workshop on Probabilistic and Statistical Methods is a meeting organized by the Join Graduate Program in Statistics UFSCar/USP (PIPGEs) with the aim of discussing new developments in statistics, probability and their applications.

Main

Activities include invited speaker sessions, short talks, a poster session and a short course devoted to graduate students. The topics of this new edition include probability and stochastic processes, statistical inference, regression models, survival analysis and related topics.

Speakers

Conferences
Alex Ramos - UFPE
Clarice Demétrio - ESALQ/USP
Juliana Cobre - ICMC/USP
Manuel Cabezas - Pontifícia Universidad Católica de Chile
Pedro Luis do Nascimento Silva - IBGE
Peter Mueller - University of Texas at Austin
Rafael Izbicki - UFSCar
Robert Gramacy - Virginia Tech

Mini-Conferences

Anderson Ara - UFBA
José Augusto Fiorucci - UnB

Short Course

Anderson Castro Soares de Oliveira - UFMT

Special Sessions

Session: Statistical methods applied to genetic data (Chair: L. A. Milan)

Benilton de Sá Carvalho, UNICAMP
Júlia Maria Pavan Soler, IME-USP
Osvaldo Anacleto, ICMC-USP


Session: Probability (Chair: A. Gallo)

Alejandra Rada, CMCC-UFABC
Élcio Lebensztayn, IMECC-UNICAMP
Ludmila Rodrigues, IME - USP

Committees

Organizing Committee

Daiane Zuanetti - UFSCar (Chair)
Katiane Conceição - ICMC/USP
Pablo Rodriguez - ICMC/USP
Ricardo Ehlers - ICMC/USP (Chair)
Sandro Gallo - UFSCar

Scientific Committee

Florencia Leonardi - IME/USP
Francisco Cribari - UFPE
Francisco Louzada Neto - ICMC/USP
Helio dos Santos Migon - UFRJ
Nancy Garcia - UNICAMP
Paulo Justiniano Ribeiro Junior - UFPR
Vera Tomazella - UFSCar

Support Committee
(students from PIPGEs)

Caio Moura Quina
Camila Sgarioni Ozelame
Deborah Bassi Stern
Gustavo Alexis Sabillón
Marcos Jardel Henriques
Victor Coscrato

Program

See below the titles and abstracts of the conferences and talks.

Conferences

Convergence time and phase transition in a non-monotonic family of probabilistic cellular automata

Alex Ramos - UFPE

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Convergence time and phase transition in a non-monotonic family of probabilistic cellular automata

Alex Ramos - UFPE

In this talk, we will consider a one-dimensional probabilistic cellular automaton where their components assume two possible states, zero and one,and interact with their two nearest neighbors at each time step. Under the local interaction, if the component is in the same state as its two neighbors, it does not change its state. In the other cases, a component in state zero turns into a one with probability α and a component in state one turns into a zero with probability 1-β. For certain values of α and β, we show that the process will always converge weakly to δ the measure concentrated on the configuration where all the components are zeros. Moreover, the mean time of this convergence is finite, and we describe an upper bound in this case, which is a linear function of the initial distribution. We also exhibit some results obtains from mean-field approximation and Monte Carlo simulations, which show coexistence of three distinct behaviours for some values of parameters α and β. This work was developed joint with A. Leite.

Reparametrization of COM-Poisson Regression Models with Applications in the Analysis of Experimental Data

Clarice Demétrio - ESALQ/USP

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Reparametrization of COM-Poisson Regression Models with Applications in the Analysis of Experimental Data

Clarice Demétrio - ESALQ/USP

In the analysis of count data often the equidispersion assumption is not suitable,hence the Poisson regression model is inappropriate. As a generalization of the Poisson distribution the COM-Poisson distribution can deal with under-, equi- and overdispersed count data. It is a member of the exponential family of distributions and has the Poisson and geometric distributions as special cases, as well as the Bernoulli distribution as a limiting case. In spite of the nice properties of the COM-Poisson distribution, its location parameter does not correspond to the expectation, which complicates the interpretation of regression models specified using this distribution. In this paper, we propose a straightforward reparametrization of the COM-Poisson distribution based on an approximation to the expectation of this distribution. The main advantage of our new parametrization is the straightforward interpretation of the regression coeficients in terms of the expectation of the count response variable, as usual in the context of generalized linear models. Furthermore, the estimation and inference for the new COM-Poisson regression model can be done based on the likelihood paradigm. We carried out simulation studies to verify the finite sample properties of the maximum likelihood estimators. The results from our simulation study show that the maximum likelihood estimators are unbiased and consistente for both regression and dispersion parameters. We observed that the empirical correlation between the regression and dispersion parameter estimators is close to zero, which suggests that these parameters are orthogonal. We illustrate the application of the proposed model through the analysis of three data sets with over-, under- and equidispersed count data. The study of distribution properties through a consideration of dispersion, zero-inflated and heavy tail indices, together with the results of data analysis show the exibility over standard approaches. Therefore, we encourage the application of the new parametrization for the analysis of count data in the context of COM-Poisson regression models. The computational routines for fitting the original and new version of the COM-Poisson regression model and the analyzed data sets are available.

Why and how to measure the reliability of scientific co-authorship networks

Juliana Cobre - ICMC/USP

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Why and how to measure the reliability of scientific co-authorship networks

Juliana Cobre - ICMC/USP

In this talk, we explain the association between a research group and a network and the different points of view that we can do it, more precisely what represents the nodes and the edges in such network. We justify why is important to measure statistically the reliability of scientific co-authorship networks, i. e., why it is not deterministic. In this study, we measure the reliability of networks by taking into account unreliable researchers and perfectly reliable edges. Some different inferential procedures presented and discussed are Bayesian inference using non-informative and informative priors. This is joint work with Sandra Cristina de Oliveira (UNESP-Tupã)

Hydrodynamic limit for the Atlas model

Manuel Cabezas - Pontifícia Universidad Católica de Chile

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Hydrodynamic limit for the Atlas model

Manuel Cabezas - Pontifícia Universidad Católica de Chile

The Atlas model is an interacting particle system where one starts with Poisson marks in [0,∞) which represent particles. As time runs, these particles perform independent Brownian motions, with the only exception being that, at any time, the leftmost particle has a drift to the right. Our main concern is to identify the possible behaviors of the leftmost particle (transience to the right, transience to the left, recurrence) as a function of the bias.

Big data: potencial, paradoxos e a importáncia renovada do pensamento estatástico

Pedro Luis do Nascimento Silva - IBGE

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Big data: potencial, paradoxos e a importância renovada do pensamento estatístico

Pedro Luis do Nascimento Silva - IBGE

Vivemos numa era em que a disponibilidade e acessibilidade a dados não tem precedentes. "Big data" é uma das tendencias deste início do Milênio a confrontar o pensamento estatístico. Por um lado, há imenso potencial para aproveitar as novas fontes de informação que se tem tornado disponíveis, acessíveis e de baixo custo. Por outro lado, lacunas substanciais persistem e há imensos riscos de utilização inadequada dessas fontes pelos que desprezam as lições traduzidas nos principais fundamentos do pensamento e da metodologia estatística. Uma das falácias principais é a de que, com as imensas bases de dados disponíveis, não será mais preciso avaliar incerteza de estimativas, pois será possível "conhecer" as quantidades de interesse a partir dos "big data". Apresenta-se o conceito de "índice de defeito dos dados" proposto por Meng (2018), e usa-se este conceito para mostrar que a qualidade de estimativas baseadas em pequenas amostras bem planejadas e executadas pode superar a de estimativas baseadas em conjuntos muito maiores provenientes de fontes orgânicas sujeitas a vieses de seleção. Penso que a metodologia estatística fornece a orientação essencial necessária para obter respostas atuais, relevantes, precisas e custo-efetivas às perguntas de interesse, mesmo na era do "big data". Apresentarei alguns exemplos ara motivar a discussão dessas ideias.

Bayesian Feature Allocation Models for Tumor Heterogeneity

Peter Mueller - University of Texas at Austin

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Bayesian Feature Allocation Models for Tumor Heterogeneity

Peter Mueller - University of Texas

We characterize tumor variability by hypothetical latent cell types that are defined by the presence of some subset of recorded SNV's. (single nucleotide variants, that is, point mutations). Assuming that each sample is composed of some sample-specific proportions of these cell types we can then fit the observed proportions of SNV's for each sample. In other words, by fitting the observed proportions of SNV's in each sample we impute latent underlying cell types, essentially by a deconvolution of the observed proportions as a weighted average of binary indicators that define cell types by the presence or absence of different SNV's. In the first approach we use the generic feature allocation model of the Indian buffet process (IBP) as a prior for the latent cell subpopulations. In a second version of the proposed approach we make use of pairs of SNV's that are jointly recorded on the same reads, thereby contributing valuable haplotype information. Inference now requires feature allocation models beyond the binary IBP. We introduce a categorical extension of the IBP. Finally, in a third approach we replace the IBP by a prior based on a stylized model of a phylogenetic tree of cell subpopulations.

ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations

Rafael Izbicki - UFSCar

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ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations

Rafael Izbicki - UFSCar

Approximate Bayesian Computation (ABC) is typically used when the likelihood is either unavailable or intractable but where data can be simulated under different parameter settings using a forward model. Despite the recent interest in ABC, high-dimensional data and costly simulations still remain a bottleneck. There is also no consensus as to how to best assess the performance of such methods. Here we show how a nonparametric conditional density estimation (CDE) framework can help address three key challenges in ABC, namely: (i) how to efficiently estimate the posterior distribution with limited simulations and different types of data, (ii) how to tune and compare the performance of ABC and related methods with CDE as a goal without knowing the true posterior, and (iii) how to efficiently choose among a very large set of summary statistics based on a CDE loss. We provide both theoretical and empirical evidence to justify the use of such procedures and describe settings where standard ABC may fail.

Replication or exploration? Sequential design for stochastic simulation experiments

Robert Gramacy - Virginia Tech

× Replication or exploration? Sequential design for stochastic simulation experiments Robert Gramacy - Virginia Tech

In this paper we investigate the merits of replication, and provide methods that search for optimal designs (including replicates), in the context of noisy computer simulation experiments. We first show that replication offers the potential to be beneficial from both design and computational perspectives, in the context of Gaussian process surrogate modeling. We then develop a lookahead based sequential design scheme that can determine if a new run should be at an existing input location (i.e., replicate) or at a new one (explore). When paired with a newly developed heteroskedastic Gaussian process model, our dynamic design scheme facilitates learning of signal and noise relationships which can vary throughout the input space. We show that it does so efficiently, on both computational and statistical grounds. In addition to illustrative synthetic examples, we demonstrate performance on two challenging real-data simulation experiments, from inventory management and epidemiology.

Mini-Conferences

Redes Bayesianas: alguns métodos e aplicações

Anderson Ara - UFBA

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Redes Bayesianas: alguns métodos e aplicações

Anderson Ara - UFBA

Redes Bayesianas, também conhecidas como redes causais, redes de crenças ou redes probabilísticas de dependência, surgiram na década de 1980 e têm aplicadas em uma ampla variedade de atividades do mundo real. Em suma, são uma representação gráfica (grafo acíclico e direcionado) das variáveis e suas relações para um problema específico, sendo tal estrutura um elemento fundamental da rede. Nesta apresentação serão expostos alguns métodos de clássicos de construção da estrutura das redes e estimação de parâmetros, bem como aplicações recentes nas áreas financeira, biológica e educacional.

Time Series Forecasting and the Makridaks Competitions

José Augusto Fiorucci - UnB

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Time Series Forecasting and the Makridaks Competitions

José Augusto Fiorucci, UnB

Accurate and robust forecasting methods for univariate time series are very important when the objective is to produce estimates for large numbers of time series. In this context, the Theta method's performance in the M3-Competition caught researchers? attention. The Theta method, as implemented in the monthly subset of the M3-Competition, decomposes the seasonally adjusted data into two "theta lines". The first theta line removes the curvature of the data in order to estimate the long-term trend component. The second theta line doubles the local curvatures of the series so as to approximate the short-term behaviour. We provide generalisations of the Theta method. The proposed Dynamic Optimised Theta Model is a state space model that selects the best short-term theta line optimally and revises the long-term theta line dynamically. The superior performance of this model is demonstrated through an empirical application. We relate special cases of this model to state space models for simple exponential smoothing with a drift.

Short Course

A utilização de redes sociais da internet para obtenção de dados

Anderson Castro Soares de Oliveira - UFMT

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A utilização de redes sociais da internet para obtenção de dados

Anderson Castro Soares de Oliveira - UFMT

À medida que as redes sociais da internet continuam se tornando integradas na vida cotidiana, os registros extensivos que estes sistemas arquivam como parte da operação normal prometem mudar os caminhos de pesquisa em várias areas de conhecimento. Assim, este minicurso discutirá o potencial da utilização destas redes sociais para levantamento de dados. Também será apresentado a limitações e dificuldades neste tipo de estudo. E por fim será apresentado alguns exemplos por meio das utilizaçao e facebook e twitter.

Special Sessions


Statistical methods applied to genetic data

Identificação de Variantes Raras em Estudos Genômicos

Benilton de Sá Carvalho - UNICAMP

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Identificação de Variantes Raras em Estudos Genômicos

Benilton de Sá Carvalho, UNICAMP

Ao longo da última década, com o desenvolvimento contínuo da tecnologia e metodologias analíticas, a geração de dados genômicos a partir de amostras biológicas teve seu custo reduzido em ordens de magnitude. Na década de 2000, o sequenciamento de um genoma completo custou aproximadamente 100 milhões de dólares. Atualmente, o custo é da ordem de 1.000 dólares, no mercado internacional. Como consequência deste processo, hoje é possível o sequenciamento de indivíduos em escalas maiores, chegando até o nível populacional. Assim, torna-se cada vez mais comum a realização de estudos para identificação de posições genômicas associadas com a ocorrência de fenótipos de interesse, como doenças complexas. O processo de análise de dados é composto por diversas etapas, que incluem filtros de diferentes tipos e modelos estatísticos para a avaliação efetiva de evidências de associação. Neste trabalho, apresentarei, da perspectiva analítica, as estratégias empregadas no Instituto Brasileiro de Neurociência e Neurotecnologia (BRAINN/FAPESP) nos estudos realizados com o intuito de identificar bases genèticas da epilepsia.

Proteogenômica: a negação do one-size-fits-all

Júlia Maria Pavan Soler - IME-USP

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Proteogenômica: a negação do one-size-fits-all

Júlia Maria Pavan Soler - IME-USP

A Proteogenômica inaugura uma nova fase de pesquisa multi-omics na Biologia Molecular, buscando integrar eficientemente grandes bancos de dados do genoma, transcriptoma e proteoma com informações clínicas. A promessa é identificar padrões específicos de pacientes e usar esse conhecimento na medicina personalizada e de precisão. Esta palestra tratará dos desafios interdisciplinares envolvidos, da abordagem e contribuição da estatística para essa área de pesquisa.

A stochastic transmission model to estimate social genetic effects in infectious diseases

Osvaldo Anacleto - ICMC-USP

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A stochastic transmission model to estimate social genetic effects in infectious diseases

Osvaldo Anacleto - ICMC-USP

Current stochastic epidemic models ignore genetic heterogeneity in infectivity, which is the propensity of an infected individual to transmit diseases. Variation in this social interaction trait leads to the common superspreading phenomenon, where a minority of highly infected hosts transmit the majority of infections. To date, is not known whether infectivity is genetically controlled. We present a novel stochastic transmission model which, by combining individual-level Poisson processes with bivariate random effects, can fully capture genetic variation in infectivity. We show that not only can this Bayesian model accurately estimate heritable variation in both infectivity and the propensity to be infected, but it also can identify parents more likely to generate offspring that are disease superspreaders. We also present a Bayesian analysis of a large-scale fish infection experiment which, for the very first time, shows that genetics does indeed contribute to variation in infectivity and therefore affects the spread of diseases.


Probability

The Shortest Possible Return Time of β-Mixing Processes

Alejandra Rada, CMCC-UFABC

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The Shortest Possible Return Time of β-Mixing Processes

Alejandra Rada, CMCC-UFABC

We consider a stochastic process and a given n-string. We study the shortest possible return time (or shortest return path) of the string over all the realizations of process starting from this string. For a β- mixing process having complete grammar, and for each size n of the strings, we approximate the distribution of this short return (properly re-scaled) by a non-degenerated distribution. Under mild conditions on the β coefficients, we prove the existence of the limit of this distribution to a non- degenerated distribution. We also prove that ergodicity is not enough to guaranty this convergence. Finally, we present a connection between the shortest return and the Shannon entropy, showing that maximum of the re-scaled variables grow as the matching function of Wyner and Ziv.

Phase transition for the frog model on trees

Élcio Lebensztayn - IMECC-UNICAMP

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Phase transition for the frog model on trees

Élcio Lebensztayn - IMECC-UNICAMP

The frog model is a stochastic epidemic model on a graph in which dormant particles begin to move and to infect other particles once they become infected. We study the frog model with geometric lifetimes on homogeneous and on biregular trees. With the help of branching processes, we obtain bounds for the critical parameter of the model.

Estimation of neuronal interaction graph from spike train data: method and application

Ludmila Rodrigues - IME-USP

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Estimation of neuronal interaction graph from spike train data: method and application

Ludmila Rodrigues, IME-USP

We address a basic question when analyzing experimental data in Neurobiology with respect to the the identification of the directed graph describing "synaptic coupling" between neurons. We present a novel estimator of effective connectivity, applying it to simulated and real data from a high quality multielectrode array recording dataset (Pouzat et al. 2015) from the first olfactory relay of the locust, Schistocerca americana. Our starting point is the procedure introduced in Duarte et al, 2016 and we present two novelties from the mathematical point of view: we propose a procedure allowing to deal with the small sample sizes met in actual datasets and we address the sensitive case of partially observed networks.




Schedule

See the poster for the meeting below, and a full list of titles and abstracts of the talks, oral communications and posters.

Schedule

Click here to download the schedule of the workshop.

Special Session: Statistical methods applied to genetic data

Speakers: Júlia Maria Pavan Soler, Osvaldo Anacleto, Benilton de Sá Carvalho.
Click here to download the schedule of the session.

Special Session: Probability

Speakers: Ludmila Rodrigues, Élcio Lebensztayn, Alejandra Rada.
Click here to download the schedule of the session.

Oral Communications

Click here to download the schedule of the oral communications.

Poster presentations

Click here to download a full list and schedule of the poster presentations.

BOOK OF ABSTRACTS

Click here to download the program of the meeting including titles and abstracts of all the presentations.




Participants

List of registered participants. Last update of the list on February 1st, 2018.
Remember that all attendees must pay the appropriate registration fee (see the Information section below for further details).

Adriano Kamimura Suzuki
Afonso Celso Penze Nunes da Cunha
Alejandra Rada
Alex de la Cruz Huayanay
Alex Leal Mota
Alex Ramos
Alex Rodrigo dos Santos Sousa
Alfredo Ribeiro de Freitas
Amanda Morales Eudes D'Andrea
Ana Carolina Freitas Xavier
Ana Paula Jorge do Espirito Santo
Anderson Ara
Anderson Castro Soares de Oliveira
André Luis Moutinho Teizen
André Luiz Tirollo dos Santos
Anna Caroline Felix Santos de Jesus
Átila Prates Correia
Benilton de Sá Carvalho
Breno Gabriel da Silva
Bruna Ambrozim Silveira
Bruna Luiza de Faria Rezende
Caio Moura Quina
Camila Sgarioni Ozelame
Carlos Alberto Oliveira de Matos
Carlos Alonso
Carlos Franklin
Carolina Grejo
Cherlynn Daniela da Silva Arce
Clarice Demétrio
Cleide Mayra Menezes Lima
Cristel Ecaterin Vera Tapia
Cristine Lemos Corrêa Amaral
Daiane Aparecida Zuanetti
Daiane de Ascenção Cardoso
Daiane de Souza Santos
Daisy Assmann Lima
Daniel Simionato
Danielle Lopes
Danillo Magalhães Xavier Assunção
Danilo Augusto Sarti
Deborah Bassi Stern
Demerson Andre Polli
Djidenou hans amos montcho
Élcio Lebensztayn
Elizbeth Chipa Bedia
Enio Junior Seidel
Felipe Aleshinsky
Felipe de Moura Ferreira
Felipe Yoshio Guskuma
Francisco Antonio Rojas Rojas
Francisco José de Almeida Fernandes
Gabriel Avila Casalecchi
Gabriel Gomes Ferreira

Gabriel Ianhez Pereira dos Santos
George Lucas Moraes Pezzott
Guilherme Antonio Alves de Lima
Guilherme Mafia
Guilherme Martins Lopes
Gustavo Alexis Sabillón Lee
Gustavo Henrique de Araujo Pereira
Henrique Trivelato de Angelo
Ianní Muliterno
Igor de Castro Chieregato
Isaac Cortés Olmos
Isis Fernanda Mascarin
Jaime Enrique Lincovil Curivil
Jaime Phasquinel Lopes Cavalcante
Jesuino Souza Araújo
Joabe Alves Carneiro
João Victor Zuanazzi Leme
João Vitor Magri da Silva
Jonathan Kevin Jordan Vasquez
José Augusto Fiorucci
Josimara Tatiane da Silva
Júlia Maria Pavan Soler
Juliana Cobre
Julio Cesar Pereira
Katiane Silva Conceição
Katy Rocio Cruz Molina
Laís Sebastiany de Souza Santos
Leandro Resende Mundim
Livia Nordi Dovigo
Lorena Yanet Cáceres Tomaya
Lucas Leite Cavalaro
Luciana Moura Reinaldo
Luciane Graziele Pereira
Ludmila Rodrigues
Luis Aparecido Milan
Luis Ernesto Bueno Salasar
Luís Felipe Barbosa Fernandes
Luis Felipe Borges de Messis
Luis Gustavo Sabino
Luis Marques da Silva
Luiz Carlos Medeiros Damasceno
Luiz Gustavo Simão Pereira
Luiz Otávio de Oliveira Pala
Luos Gustavo Sabino
Manuel Cabezas
Marcelo Andrade da Silva
Márcio Luis Lanfredi Viola
Marcos Jardel Henriques
Maria Lígia Chuerubim
Maria Silvia de Assis Moura
Mariane Romildo dos Santos
Marina Gandolfi

Marina Gonzaga de Oliveira
Mário de Castro
Matheus dos Santos Barbosa da Silva
Milena Nascimento Lima
Milton Miranda Neto
Mirian Wawrzyniak Chimirri
Murilo Henrique Soave
Naiara Caroline Aparecido dos Santos
Nancira Riberio Madi
Nancy Garcia
Nayara Fernandes de Mendonça
Oilson Alberto Gonzatto Junior
Oscar Holguin Villamil
Osvaldo Anacleto
Pablo Rodriguez
Patty Mercedes Arce Flores
Paulo Freitas Gomes
Paulo Oliveira
Pedro Ferreira Filho
Pedro Floriano Ribeiro
Pedro Luis do Nascimento Silva
Peter Müller
Rafael Izbicki
Rafaela Cristina de Camargo
Renan Douglas Floriano Scavazzini
Renata Cristina da Penha Silveira
Ricardo Gonçalves da Silva
Ricardo Sandes Ehlers
Robert Gramacy
Roberta de Souza
Rodrigo Barrem
Samirian
Sandra Cristina de Oliveira
Sandro Gallo
Sandro Gonçalves
Sandro Martinelli Reia
Solange Ferreira Silvino
Steve Ataucuri Cruz
Tainá Santana Caldas
Taís Roberta Ribeiro
Talita Zara Crevelim
Tatyana Zabanova
Thiago Gottardi
Thiago Roberto do Prado
Vanessa Helena Pereira
Vera Tomazella
Victor Azevedo Coscrato
Vinicius Hideki Yamada Santiago
Vitor Gustavo de Amorim
Waldomiro Barioni Júnior
Walkiria Maria de Oliveira Macerau
Yana Miranda Borges
Yury Rojas Benites




Information

For additional information, please contact us here. We will contact you back as soon as possible.

Important Dates

Deadline abstract submission: Jan 18th
Notification of acceptance: Jan 28th
Deadline for early registration: Feb 1st

Early Fees* (before/on Feb 1st)

Researchers/others: R$ 60,00
Graduate students: R$ 40,00
Undergraduate students: R$ 20,00

Regular Fees** (after Feb 1st)

Researchers/others: R$ 70,00
Graduate students: R$ 50,00
Undergraduate students: R$ 25,00

Where at ICMC-USP

Registration and main activities will be held at the Auditório Fernão Stella de Rodrigues Germano. Bloco-6 Térreo, Sala 6-001.

Hotel

For lodging and accommodations we recommend Sleep Inn São Carlos


*Early fees must be paid at Associação Brasileira de Estatística CNPJ 56572456/0001-80: Banco Santander (033), Agência 0658, C/C 13006798-9, and the receipt must be sent to secretaria@redeabe.org.br with the subject [pagamento 7WPSM]. (O pagamento antecipado deve ser realizado por transferência bancária para Associação Brasileira de Estatística CNPJ 56572456/0001-80: Banco Santander (033), Agência 0658, C/C 13006798-9, e o comprovante do depósito deve ser enviado por e-mail para secretaria@redeabe.org.br com o assunto [pagamento 7WPSM])
**Regular fees may be paid in cash at the registration desk on the first day.

Past Editions

See below some information related to the previous editions of our meeting.

VI WPSM

5, 6, 7 February 2018, UFSCar
Book of Abstracts

V WPSM

6, 7, 8 February 2018, ICMC-USP
Book of AbstractsPoster

IV WPSM

1, 2, 3 February 2016, UFSCar
Book of AbstractsPoster

III WPSM

9, 10, 11 February 2015, ICMC-USP
Book of AbstractsPoster

II WPSM

5, 6, 7 February 2014, UFSCar
Book of AbstractsPoster

WPSM

28, 29, 30 January 2013, ICMC-USP
Book of Abstracts



Organization


      


Support


                                    

The meeting in numbers

Since the first edition of the Workshop on Probabilistic and Statistical Methods, helded in 2013, we have received the contribution of many colleagues and students.

Conferences and Talks
Oral Communications
Poster Presentations
Participants

Questions?

If you have any question, please do not hesitate in contact us! We are looking forward to seeing you in São Carlos.

Contact Info

E-mail: wpsm@icmc.usp.br

PIPGEs at UFSCar
DEs-Universidade Federal de São Carlos
Rod. Washington Luiz, km 235
CEP: 13565-905 - São Carlos - SP - Brazil

PIPGEs at USP
ICMC - Universidade de São Paulo
Av. Trabalhador São-carlense, 400, Centro
CEP: 13566-590 - São Carlos - SP - Brazil
© 2018 PIPGEs UFSCar/USP.