Using Storm for scaleable sequential statistical inference.

Wilson, S.P.; Mai, T.; Cogan, P.; Bhattacharya, A.; Robles-Sánchez, O.; Aslett, L.J.M.; Ó Ríordáin, S.; Roetzer, G.

Abstract

This article describes Storm, an environment for doing streaming data analysis. Two examples of sequential data analysis — computation of a running summary statistic and sequential updating of a posterior distribution — are implemented and their performance is investigated.

Citation

Wilson, S., Mai, T., Cogan, P., Bhattacharya, A., Robles-Sánchez, O., Aslett, L., …Roetzer, G. (2014). Using Storm for scaleable sequential statistical inference. In M. Gilli, G. González-Rodríguez, & A. Nieto-Reyes (Eds.), Proceedings of COMPSTAT 2014: 21st International Conference on Computational Statistics (hosting the 5th IASC World Conference): Geneva, Switzerland, August 19–22, 2014 (103-109)

• Conference Name: 21st International Conference on Computational Statistics (COMPSTAT 2014)
• Conference Location: Geneva, Switzerland
• Online Publication Date: Aug 19, 2014
• Publication Date: 2014
• Deposit Date: Apr 24, 2017
• Publicly Available Date: Nov 8, 2023
• Pages: 103-109
• Book Title: Proceedings of COMPSTAT 2014: 21st International Conference on Computational Statistics (hosting the 5th IASC World Conference): Geneva, Switzerland, August 19–22, 2014.
• Public URL: https://durham-repository.worktribe.com/output/1147129