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.

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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