Bayesian computational statistics

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.

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.

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.

Miguel Angel Alvarez Ballesteros

Miguel Angel Alvarez Ballesteros is a M.S./Ph.D. candidate in the KAUSTStochastic Numerics research group under the supervision of Professor Raul Tempone. Before joining KAUST, Miguel obtained a bachelor's degree in physics from the Universidad Industrial de Santander, Bucaramanga, Colombia. Research Interests Miguel's research interests include Monte Carlo methods, Bayesian Statistics, Particle filtering and Machine learning. Education Profile Bachelor's degree in Physics from the Universidad Industrial de Santander (UIS), Bucaramanga, Colombia, in 2019. Publications Álvarez, M., Orjuela

Haavard Rue

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