Bayesian machine learning
Contact person: Geir Storvik
Keywords: Bayesian methodology, Machine learning, neural nets
Research group: Statistics and Data Science
Department of Mathematics
Bayesian approaches to machine learning are of interest both due to the possibility of incorporating knowledge into the learning process and due to the coherent way of taking uncertainty into account. Due to computational constraints, various approximation techniques are typically applied. How much one loses in using such approximations is unclear. Knowledge is usually incorporated through a probabilistic description (a prior). What properties such priors, typically described on latent variables in complex networks, have is largely unknown. Further, priors that are used are typically very general and generally do not consider real knowledge.
Methodological research topics:
- Prior specifications for learning neural networks
- Combining recent advances within general Markov chain Monte Carlo algorithms and subsampling approaches for computationally efficient Bayesian neural networks
- Sequential Monte Carlo for Bayesian machine learning
- Conflict diagnostics and sensitivity analysis in Bayesian machine learning
External partners:
- Norwegian Computing Center (NR)
Mentoring and internship will be offered by a relevant external partner
