SCORE-MATCHING ESTIMATORS FOR CONTINUOUS-TIME POINT-PROCESS REGRESSION MODELS

被引：0

作者：

Sahani, Maneesh ^{[1
]}

Bohner, Gergo ^{[1
]}

Meyer, Arne ^{[1
]}

机构：

[1] UCL, Gatsby Computat Neurosci Unit, 25 Howland St, London W1T 4JG, England

来源：

2016 IEEE 26TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2016年

关键词：

point-process; score matching; estimation; spike train; neural data;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：5

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