Sahani, M;
Bohner, G;
Meyer, A;
(2016)
Score-matching estimators for continuous-time point-process regression models.
In:
Proceedings of MLSP2016.
IEEE
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Abstract
We introduce a new class of efficient estimators based on score matching for probabilistic point process models. Unlike discretised likelihood-based estimators, score matching estimators operate on continuous-time data, with computational demands that grow with the number of events rather than with total observation time. Furthermore, estimators for many common regression models can be obtained in closed form, rather than by iteration. This new approach to estimation may thus expand the range of tractable models available for event-based data.
Type: | Proceedings paper |
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Title: | Score-matching estimators for continuous-time point-process regression models |
Event: | 26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP) |
Location: | Salerno, ITALY |
Dates: | 13 September 2016 - 16 September 2016 |
ISBN-13: | 978-1-5090-0747-9 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/MLSP.2016.7738848 |
Publisher version: | https://doi.org/10.1109/MLSP.2016.7738848 |
Language: | English |
Additional information: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Science & Technology, Technology, Engineering, Electrical & Electronic, Engineering, point-process, score matching, estimation, spike train, neural data |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1543256 |
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