Bayesian computational statistics

Truth, Knowledge, P-Values, Bayes, & Inductive Inference

-

B1 L4 R4102

The talk will concern the role of statistical thinking in the search for Truth. This will bring us to a discussion of P-values, which has been, and still is, a central concept in Statistics and is a critical and highly controversial tool of scientific research. Recently it has elicited much, sometimes heated, debates and discussions. The American Statistical Association (ASA) was even compelled to release an official statement in early March 2016 regarding this issue, and a psychology journal gone to the extreme of banning the use of P-values in articles appearing in its journal!

Exploring Non-Linear Interactions in Multivariate Time Series

-

KAUST

Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.

Joint Posterior Inference for Latent Gaussian Models and extended strategies using INLA

-

B3 L5 R5209

Bayesian inference is particularly challenging on hierarchical statistical models as computational complexity becomes a significant issue. Sampling-based methods like the popular Markov Chain Monte Carlo (MCMC) can provide accurate solutions, but they likely suffer a high computational burden.

Bayesian Deep Learning

Maurizio Filippone, Associate Professor, EURECOM, France

-

B9 L3 R3128

The impressive success of Deep Learning (DL) in predictive performance tasks has fueled the hopes that this can help addressing societal challenges by supporting sound decision making. However, many open questions remain about their suitability to hold up to this promise. In this talk, I will discuss some of the current limitations of DL, which directly affect their wide adoption. I will focus in particular on the poor ability of DL models to quantify uncertainty in predictions, and I will present Bayesian DL as an attractive approach combining the flexibility of DL with probabilistic reasoning. I will then discuss the challenges associated with carrying out inference and specifying sensible priors for DL models. After presenting a few of my contributions to address these problems, I will conclude by presenting some interesting emerging research trends and open problems which define my current research agenda.

Approximate Bayesian inference based on dense matrices and new features using INLA

-

B4 L5 R5220

The Integrated Nested Laplace Approximations (INLA) method has become a commonly used tool for researchers and practitioners to perform approximate Bayesian inference for various fields of applications. It has become essential to incorporate more complex models and expand the method’s capabilities with more features. In this dissertation, we contribute to the INLA method in different aspects.

2015 Workshop on Computational Space-Time Statistics

1 min read ·
Organizers : Prof. Marc G. Genton , Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. Sudipto Banerjee, University of California Los Angeles, USA Prof. Souhaib Ben Taieb, Monash University, Australia Prof. Stefano Castruccio, Newcastle University, UK Prof. Miguel de Carvalho, Pontificia Universidad Catolica de Chile,​ Chile Prof. Clément Dombry, University of Franche-Comté , France Prof. Xuming He, University of Michigan, USA Prof. Hsin-Cheng Huang

Solving real-world problems

1 min read ·
A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

KAUST Ph.D. candidate wins ISBA-BayesComp poster award at the International Society for Bayesian Analysis 2022

1 min read ·
Esmail Abdul Fattah, a KAUST Ph.D. candidate in statistics, won a poster award in the "junior-BayesComp-ISBA" category at the 30th meeting of the International Society for Bayesian Analysis (ISBA 2022) held from June 26 to July 1 in Montreal, Canada.

Zhedong Liu

Ph.D. Student, Statistics
Ph.D. degree in Statistics at the King Abdullah University of Science and Technology (KAUST).

Małgorzata Forystek

M.S. Student, Statistics
Statistics Master's student interested in Data Science

Janet van Niekerk

Research Scientist, Statistics
Janet van Niekerk is a Research Scientist at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career MSc (Mathematical Statistics), University of Pretoria, 2014 PhD (Mathematical Statistics), University of Pretoria, 2017 Research interests Efficient Bayesian methods for practical implementation, statistics for medical applications, complex survival analysis, INLA. Awards World Bank Fund awardee, International Statistical Institute, 2013 and 2015 Best oral presentation, Statistical

Hanan Alahmadi

Ph.D. Student, Statistics
Hanan Alahmadi is a PhD candidate in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Paula Moraga in her Geospatial Statistics and Health Surveillance Research Group. Education Profile M.Sc. Statistics, KAUST, 2021 B.Sc. Mathematics, Taibah University , 2017 Research Interest Geospatial data analysis and health surveillance Spatio-temporal disease data Statistics and spatial epidemiology Geospatial modeling Disease mapping Location Building 1, Al-Khawarizmi, 4th floor, 4212-WS12 Publication Joint Quantile Disease Mapping

Haakon Bakka

Postdoctoral Research Fellow, Statistics
Haakon Bakka is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Haakon received his PhD degree from the Norwegian University of Science and Technology in Trondheim, Norway. Research Interest My focus is on applied Bayesian statistics, often with spatial or space-time models, including priors, constructing random effects, fast inference, software development and data analysis.

Haavard Rue

Program Chair, Statistics
Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics.

Gulzhan Tumenbayeva

Ph.D. Student, Statistics
Gulzhan Tumenbayeva is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Gulzhan obtained her Bachelor of Science in Mathematics, Diploma with Honors in 2017 from Nazarbayev University, Astana, Kazakhstan. Then, she joined the MS/PhD program in Statistics at KAUST in 2017. In 2018, she graduated her Master of Science in Statistics at KAUST. Research Interest Her research interest is mainly in Bayesian and computational Statistics as well as

Elias Teixeira Krainski

Postdoctoral Research Fellow, Statistics
Elias T Krainski is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Postdoctoral fellow, Dalhousie University, 2019-2020. Adjunct professor, Universidade Federal do Paraná, 2018-2019. PhD, Norwegian University of Science and Tecnology, 2018. Research interests Efficient Bayesian methods for practical implementation, Spatio and spatio-temporal statistics, INLA.

Djidenou Montcho

Ph.D. Student, Statistics
Djidenou (Hans) Montcho is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Hans obtained his Bachelor Degree in Applied Mathematics at the Federal University of Rio Grande in Brazil. He graduated with a Master of Science from the Federal University of Sao Carlos and the University of Sao Paulo. He has worked as a data scientist for a large credit bureau in Brazil. Research Interest His research interest is mainly in Bayesian and

Denis Rustand

Postdoctoral Research Fellow, Statistics
Denis Rustand is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career MSc (Statistics), University of Southern Brittany, 2017 PhD (Biostatistics), University of Bordeaux, 2020 Research interests Bayesian computational statistics, survival analysis, applications of statistics to medical research, INLA. Awards Doctor Norbert Marx Award, 2021 Summer school grant, Univerity of Toronto, 2018 Académie Française, Jean Walter Zellidja grant, 2018 EHESP

Cristian Chiuchiolo

Cristian Chiuchiolo is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Christian obtained his Bachelor Degree in Statistics in 2015 at Florence, Italy. H e graduated with a Master of Science in Statistics at Bologna, Italy in 2017. Research Interest His research interest is mainly in Bayesian and computational Statistics as well as Probability and applications in R-INLA. Honors and Awards Cristian was announced a SAS Certified Base

Abylay Zhumekenov

Ph.D. Student, Statistics
Abylay Zhumekenov is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Abylay obtained his Bachelor of Science in Mathematics in 2017 from Nazarbayev University, Astana, Kazakhstan. He then obtained a Master of Science degree in Applied Mathematics from Nazarbayev University in 2019. Research Interests Abylay's main research interests are Bayesian computational statistics and numerical methods for large sparse systems.