bayesian inference

Manifold Markov Chain Monte Carlo Methods for Bayesian Inference In Diffusion Models

Prof. Alexandros Beskos, the Dept of Statistical Science, University College London (UCL)

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KAUST

Bayesian inference for nonlinear diffusions, observed at discrete times,is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompanying methodology - borrowing ideas from statistical physics and computational chemistry - for inferring the posterior distribution of latent diffusion paths and model parameters, given observations of the process

Bayesian Modeling of Sub-Asymptotic Spatial Extremes

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B5 L5 R5220

In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.

The role of skewed distributions in Bayesian inference: conjugacy, scalable approximations and asymptotics

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B1 L4 R4102

In this talk, I will review, unify and extend recent advances in Bayesian inference and computation for such a class of models, proving that unified skew-normal (SUN) distributions (which include Gaussians as a special case) are conjugate to the general form of the likelihood induced by these formulations. This result opens new avenues for improved posterior inference, under a broad class of widely-implemented models, via novel closed-form expressions, tractable Monte Carlo methods based on independent and identically distributed samples from the exact SUN posterior, and more accurate and scalable approximations from variational Bayes and expectation-propagation. These results will be further extended, in asymptotic regimes, to the whole class of Bayesian generalized linear models via novel limiting approximations relying on skew-symmetric distributions.

KAUST professors receive Abdul Hameed Shoman Award for Arab Researchers

1 min read ·
Mohamed Eddaoudi, KAUST distinguished professor of chemical science and director of the University's Advanced Membrane & Porous Materials Center, recently received the Abdul Hameed Shoman Award for Arab Researchers for Renewable Energy, an award presented annually by the Abdul Hameed Shoman Foundation. Omar Knio, KAUST professor of applied mathematics and computational science, was also honored by the Abdul Hameed Shoman Foundation this year as a joint recipient of the Shoman Award for Mathematical Modeling with Dr. Shaher Mohammed Ahmad Momani.

Rishikesh Yadav

Ph.D. Student, Statistics
Rishikesh Yadav was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Rishikesh successfully defended his PhD thesis entitled " Bayesian Modeling of Sub-Asymptotic Spatial Extremes" on April 5th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Philippe Naveau (external examiner from CNRS, France), Marc Genton, and Ajay Jasra. For his next career steps, Rishikesh has accepted a postdoctoral position at HEC Montréal, Canada, under the joint supervision

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Omar Knio

Professor Knio focuses on developing state-of-the-art methods and algorithms for simulating complex multiscale systems, and their implementation to the analysis and optimization of renewable energy systems.

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group
Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.