Events

Jun 4 - Jun 10, 2023

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

  Jun 4, 15:00 - 16:00

  B4 L5 R5220

  Bayesian computational statistics

  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.

May 28 - Jun 3, 2023

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  Leave-Group-Out Cross-Validation for Latent Gaussian Models

  Zhedong Liu, Ph.D. Student, Statistics

  May 30, 15:30 - 17:30

  B1 R4102;

  The commonly used leave-one-out and K-fold cross-validation methods are not suitable for structured models with multiple prediction tasks. To overcome this limitation, we introduce leave-group-out cross-validation, which allows groups to adapt to different tasks. We propose an automatic group construction method and provide an efficient approximation for latent Gaussian models. Moreover, this method is conveniently implemented in the R-INLA software.
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  Criticism and robustification of latent Gaussian models

  Rafael Medeiros Cabral, Ph.D. Student, Statistics

  May 28, 15:00 - 16:00

  B1 L4 R4102

  latent Gaussian models

  Latent Gaussian models (LGM) are widely used but struggle with certain datasets that contain non-Gaussian features, such as sudden jumps or spikes. This dissertation aims to provide tools for researchers to check the adequacy of the fitted LGM (criticism); if the check fails, offer efficient and user-friendly implementations of latent non-Gaussian models, which lead to more robust inferences (robustification).

Jun 5 - Jun 11, 2022

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

  Cristian Chiuchiolo, Ph.D. Student, Electrical and Computer Engineering

  Jun 6, 15:00 - 17:00

  B3 L5 R5209

  Bayesian computational statistics Bayesian Statistics

  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.

Feb 13 - Feb 19, 2022

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  Causal reasoning in settings with competing events and truncation by death

  Prof. Mats Julius Stensrud, Department of Mathematics, EPFL

  Feb 16, 16:00 - 17:00

  B9 L2 R2322

  A competing event is any event that makes it impossible for the outcome of interest to occur. The presence of competing events requires us to be careful about the interpretation of classical causal estimands. In particular, the average treatment effect captures effects through the competing event, pathways that may not be of primary interest. As a solution, we suggest the separable effect, inspired by Robins and Richardson’s extended graphical approach. We will give criteria that allow different interpretations of the separable effects and present identification conditions that can be evaluated in causal graphs.

Nov 7 - Nov 13, 2021

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  Joint Quantile Disease Mapping for Areal Data

  Hanan Alahmadi, Ph.D. Student, Statistics

  Nov 7, 15:00 - 16:00

  B1 L4 R4102

  The statistical analysis based on the quantile method is more comprehensive, flexible, and not sensitive against outliers compared to the mean methods. The study of the joint disease mapping focuses on the mean regression. This means they study the correlation or the dependence between the means of the diseases by using standard regression. However, sometimes one disease limits the occurrence of another disease. In this case, the dependence between the two diseases will not be in the means but in the different quantiles; thus, the analyzes will consider a joint disease mapping of high quantile for one disease with low quantile of the other disease.

Feb 23 - Feb 29, 2020

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  Block Nearest Neighbor Gaussian processes for large data

  Dr. Zaida Quiroz Cornejo

  Feb 25, 14:00 - 15:30

  B1 L4 R4102

  Abstract We develop a valid spatial block-Nearest Neighbor Gaussian process (block-NNGP) for estimation and prediction of location-referenced large spatial data. The key idea behind the block-NNGP is to subdivide the spatial domain into several blocks which are dependent under some constraints. The cross-blocks capture the large-scale spatial variation, while each block capture the small-scale dependence. The block-NNGP is embedded as a sparsity-inducing prior within a hierarchical modeling framework. Markov chain Monte Carlo (MCMC) algorithms are executed without storing or decomposing large

Feb 16 - Feb 22, 2020

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  Statistical models for long-range dependent climate data

  Sigrunn Sorbye, Associate Professor, UiT The Arctic University of Norway

  Feb 20, 12:00 - 13:00

  B9 L2 R2322

  statistical models climate data

  In this talk I will discuss statistical models which incorporate temperature response to the radiative forcing components. The models can be used to estimate important climate sensitivity measures and give temperature forecasts. Bayesian inference is obtained using the methodology of integrated nested Laplace approximation and Monte Carlo simulations. The resulting approach will be demonstrated in analyzing instrumental data and Earth system model ensembles.

Feb 9 - Feb 15, 2020

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  The weather simulation game: an introduction to stochastic weather models with an application to simulating precipitation on Norway

  Prof. Sara Martino, NTNU, Norway

  Feb 11, 14:00 - 15:30

  B1 L4 R4102

  Stochastic weather generators (SWG) are statistical models designed to provide arbitrarily long realistic simulations of atmospheric variables such as temperature , precipitation and wind at specific locations or areas, at daily or hourly temporal scales. Such simulated series serve as input for environmental, ecological or agricultural impact studies or for long time planning of renewable energy resources. In this talk I'll introduce the main ideas behind this models. Afterwards I will present a spatio-temporal generator based on a latent Gaussian field and its application to simulating daily

Nov 17 - Nov 23, 2019

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  2019 Statistics and Data Science Workshop

  Nov 18, 00:00 - Nov 20, 23:45

  B2 B3 A0215

  2019 Statistics and Data Science Workshop confirmed speakers include Prof. Alexander Aue, University of California Davis, USA, Prof. Francois Bachoc, University Toulouse 3, France, Prof. Rosa M. Crujeiras Casais, University of Santiago de Compostela, Spain, Prof. Emanuele Giorgi, Lancaster University, UK, Prof. Jeremy Heng, ESSEC Asia-Pacific, Singapore, Prof. Birgir Hrafnkelsson, University of Iceland, Iceland, Prof. Ajay Jasra, KAUST, Saudi Arabia, Prof. Emtiyaz Khan, RIKEN Center for Advanced Intelligence Project, Japan, Prof. Robert Krafty, University of Pittsburgh, USA, Prof. Guido Kuersteiner, University of Maryland, USA, Prof. Paula Moraga, University of Bath, UK, Prof. Tadeusz Patzek, KAUST, Saudi Arabia, Prof. Brian Reich, North Carolina State University, USA, Prof. Dag Tjostheim, University Bergen, Norway, Prof. Xiangliang Zhang, KAUST, Saudi Arabia, Sylvia Rose Esterby, University of British Colombia, Canada, Prof. Abdel El-Shaarawi, Retired Professor at the National Water Research Institute, Canada. View Workshop schedule and abstracts here.

Nov 10 - Nov 16, 2019

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  Measurement Error Modeling

  Dr. Erica Ponzi

  Nov 11, 15:00 - 16:30

  B1 L2 R2202

  Dr. Erica Ponzi from the University of Oslo will present a talk with discussion on the modeling of measurement error.
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  Multivariate Conditional Autoregressive Models

  Dr. Anna Freni-Sterrantino, Imperial College, London

  Nov 10, 15:00 - 16:30

  B1 L4 R4214

  Dr. Anna Freni-Sterrantino is currently collaborating with Prof. Haavard Rue to develop methodology to implement Multivariate Conditional Autoregressive Models in R-INLA. This event is in the form of a discussion group on current progress and future plans.

May 5 - May 11, 2019

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  Joint longitudinal-survival models using R-INLA

  Janet van Niekerk, Research Scientist, Statistics

  May 9, 12:00 - 13:00

  B9 L2 H1

  stochastic smoothing pipeline R-INLA

  Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and non-linear longitudinal trajectories amongst others. They gained popularity amongst practitioners by the ability to incorporate various data sources. In this talk, we will introduce joint models and provide some conceptual ideas about their use and necessity. Also, we will illustrate how these models can be formulated as Latent Gaussian Models and hence be implemented using R-INLA.

Nov 11 - Nov 17, 2018

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  2018 Workshop on Statistics and Data Science

  Nov 12, 08:30 - Nov 15, 17:00

  Auditorium 0215 between Buildings 4 & 5

  data science high dimensional statistics modeling biological processes physical processes

  The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.