Franklin

Building a platform for data-driven pandemic prediction : from data modelling to visualisation - the CovidLP Project / edited by Dani Gamerman, Marcos O. Prates, Thais Paiva, Vinicius D. Mayrink.

Publication:
Boca Raton : Chapman & Hall/CRC, 2022.
Format/Description:
Book
1 online resource (xviii, 364 pages)) : illustrations (black and white).
Edition:
First edition.
Status/Location:
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Subjects:
Epidemics -- Forecasting -- Data processing.
Medical statistics -- Data processing.
Biography/History:
Dani Gamerman was Professor of Statistics at UFRJ from 1996 to 2019. He is currently Professor Emeritus at UFRJ since 2021 and Visiting Professor at UFMG since 2019. Director of the Graduate Program in Statistics at UFRJ (1999-2006 and 2015-2019). Published papers at JRSSB, Biometrika, Statistics and Computing, Bayesian Analysis, Journal of Multivariate Analysis, Applied Statistics and other journals. Author of books Monte Carlo Markov Chain: Stochastic Simulation for Bayesian Inference and Statistical Inference: an Integrated Approach, both with Chapman & Hall in their 2nd edition. Supervised 19 M.Sc. and 18 Ph.D. students. Visiting professor of a number of academic institution world-wide. Delivered seminars at many scientific meetings and universities world-wide, including plenary talks at a Valencia meeting and an ISBA world meeting. Editor of 5 statistical journals. Organized many statistical conferences in Brazil. Marcos Prates obtained his bachelor's in 2006 in the Computational Mathematics program at the Universidade Federal de Minas Gerais (UFMG) and a master's in Statistics in 2008 from the same institution. In 2011 he received his Ph.D. in Statistics from the University of Connecticut and was a Visiting Professor in the same institution from 2019 to 2020. Currently, he is an Associate Professor at UFMG. His main research areas are Bayesian Statistics, Generalised Linear Mixed Models, Machine Learning, and Spatial Statistics. He was Director of the Graduate Program in Statistics in UFMG (2016-2018), was the Secretary for ISBRA, the Brazilian chapter of ISBA (2015-2016), and currently is the President of the Brazilian Statistical Association (2020-2022). Thais Paiva obtained a bachelor degree in Actuarial Science from the Universidade Federal de Minas Gerais (UFMG) in 2008, and a Masters in Statistics from the same university in 2010. She earned a PhD degree in Statistics at Duke University in 2014. Since 2016, she has been an Assistant Professor in the Statistics Department at UFMG, working actively on the Actuarial Science undergraduate program and on the Statistics graduate program. Her main research interests are Bayesian Statistics, Imputation Methods for Missing Data, Data Confidentiality and Spatial Statistics. Vinicius Mayrink is an Associate Professor in the Department of Statistics at the Universidade Federal de Minas Gerais (UFMG) in Brazil. He received: his Ph.D. degree in the Department of Statistical Science at Duke University (USA, 2011), B.Sc. degree in Statistics from UFMG (2004), M.Sc. degree in Statistics from the Universidade Federal do Rio de Janeiro (2006) and a second M.Sc. degree in Statistics at Duke University (2009). Vinicius is currently the sub-Director of the Graduate Program in Statistics of the UFMG (2021-2023). He was a member (treasurer, 2015-2016) of the administrative board of ISBRA (the Brazilian chapter of ISBA). His research interests include: Bayesian Inference, Multivariate Analysis, Spatial Statistics, Survival Analysis and Statistical Modeling in Bioinformatics.
Notes:
"Chapman & Hall book"
Includes bibliographical references and index.
Electronic reproduction. London Available via World Wide Web.
Description based on online resource; title from digital title page (viewed on October 14, 2021).
Contributor:
Gamerman, Dani, editor.
Prates, Marcos O., editor.
Paiva, Thais, editor.
Mayrink, Vinicius D., editor.
Taylor & Francis eBooks
Other format:
Print version: Building a platform for data-driven pandemic prediction.
ISBN:
9781003148883
1003148883
9781000457193
1000457192
9781000457223
1000457222
9780367709990
0367709996
9780367709976
036770997X
Publisher Number:
99989315545
Access Restriction:
Restricted for use by site license.