Modelling changepoints in a Bayesian way is elegant and computationally efficient. I am currently working to extend this into a spatio-temporal context and to enable scalable robust inference on multivariate data.
- Knoblauch, J. & Damoulas T. (2018). Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection, International Conference on Machine Learning (ICML 2018).
- Knoblauch, J., Jewson, J. & Damoulas T. (2018). Doubly Robust Bayesian Inference for Non-Stationary Streaming Data using β-Divergences (NIPS 2018)
Recent & Upcoming Talks
- Bayesian On-line Changepoint Detection and Model Selection in high-dimensional data, Workshop on Computational Strategies for Large-Scale Statistical Data Analysis by the International Centre for Mathematical Sciences, Edinburgh 05/07/2018
- Bayesian Analysis for Non-Stationary Streaming Data, Seminar Series of the CDT in Data Science, University of Edinburgh, 04/07/2018
- Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection, Statistics Seminar of the Glasgow School of Mathematics & Statistics, University of Glasgow, 06/07/2018
- Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection, International Conference on Machine Learning (ICML), Stockholm (10/07/2018-15/07/2018) [Talk and poster presentations]
Recent & Upcoming Talks by Collaborators
My friend and collaborator Jack Jewson will be presenting novel robust Bayesian techniques for streaming data which we recently started developing together:
- World Meeting of the International Society for Bayesian Analysis (ISBA), Edinburgh (25/06/2018-29/06/2018) [poster presentation]
- Bayesian Young Statisticians Meeting (BAYSM), University of Warwick (02/07/2018-03/07/2018)
I have written a substantial amount of software in Python accompanying my research, through which we have been nominated as Turing Reproducible Research Champions 2018 by the Alan Turing Institute. While it is still being worked on, the preliminary version is available here.
Office: C1.02, Zeeman building
Personal webpage: https://jeremiasknoblauch.github.io/