Poster presentations 2017

The following posters will be presented during the conference:
Posters will be presented 12.30 – 14.00 on Wednesday 28th June 2017, but the poster boards will be available throughout the conference and contributors can leave submissions on display to be browsed at leisure.


1. Anomaly Stream Detection Based on Parallel and Distributed Computing – Bakhtiar Amen, Grigoris Antoniou, Violeta Holmes and Ilias Tachmazidis – view abstract

2. Bayesian inference on high-dimensional Seemingly Unrelated Regression, applied to metabolomics data - M. Banterle, A. Lewin – view abstract

3. Online conditional variance estimation for massive functional ergodic processes - Mohamed Chaouch – view abstract

4. On big data based statistical inference - Piet Daas, Marco Puts and Robert Renssen – view abstract

5. Multiple Changepoint Estimation in High-Dimensional Gaussian Graphical Models – Alex Gibberd and Sandipan Roy – view abstract

6. Big data analysis of heterogeneous and complex time series - Arthur P. Guillaumin, Adam M. Sykulski, So fia C. Olhede, Jeffrey J. Early, Jonathan M. Lilly – view abstract

7. Kinetic energy choice in Hamiltonian Monte Carlo - Samuel Livingstone – view abstract

8. High-Dimensional Uncertainty Estimation with Sparse Priors for Radio Interferometric Imaging - Xiaohao Cai, Marcelo Pereyra, Jason D. McEwen – view abstract

9. Robust timestamps and privacy preservation for inference from longitudinal data - Anna Palczewska, Georgios Aivaliotis, Jan Palczewski – view abstract

10. Joint estimation of multiple penalized graphs using zoom-in/out penalties - Eugen Pircalabelu, Gerda Claeskens, Lourens Waldorp – view abstract

11. Detecting interactions in highly multivariate point patterns using variable selection - Tuomas Rajala, Sofia Olhede, David Murrell – view abstract

12. Spatio-temporal modelling for global sea level change - Zhe Sha, Andrew Zamit-Mangion, Jonathan C. Rougier, Maike Schumacher, William Llovel, and Jonathan L. Bamber – view abstract

13. Big Data methodology for large-scale spatiotemporal oceanographic datasets - Adam M. Sykulski, Jonathan M. Lilly, Sofi a C. Olhede, Jeffrey J. Early – view abstract

14. Learning to Predict: Estimating the Structure of the Non-stationary Spatiotemporal Profiles in Big Data for Behaviour Prediction - Anastasia Ushakova – view abstract

15. Bayesian Inference for High Dimensional Dynamic Spatio-Temporal Models – (Sofia Maria Karadimitriou, Kostas Triantafyllopoulos, and Timothy Heaton) – view abstract