About the project
In this project we develop new models, methods and algorithms for Bayesian machine learning, in particular related to deep neural networks and computational causal inference.
Sub-projects
- Bayesian estimation of causal effects using directed graphical models. PhD project for Vera Kvisgaard.
- A Bayesian model-averaging toolkit for causal inference with observational data under nonlinear structural equations: An application to the effect of ADHD treatment on school performance by Norwegian children. PhD project for Johan de Aguas.
- A fully probabilistic methodology for providing diverse, personalised recommendations from clicking data, using a Variational Bayesian approach for fast computation. Master student: Haakon Muggerud
- Normalizing flows as variational inference approximations in latent binary Bayesian neural network models. Master project by Lars Skaaret-Lund
- A fully probabilistic methodology for providing diverse, personalised recommendations from clicking data, using a Variational Bayesian approach for fast computation. Master student: Haakon Muggerud
- Subsampling Strategies for Bayesian Variable Selection and Model Averaging in GLM and BGNLM. Master project by Jon Lachmann (Stockholm University)
- Identification of non-linear Models with a Bayesian Model selection Tool. Master project by Elke Bruns (University of Vienna)
Financing
- Aliaksandr Hubin is hired as a post doc through the "Akademia-avtale" with Equinor
- Part of the activity is financed through BigInsight