Variational bayesian inference for point process generalized linear models in neural spike trains analysis
Point process generalized linear models (GLMs) have been widely used for neural spike trains analysis. Statistical inference for GLMs include maximum likelihood and Bayesian estimation. Variational Bayesian (VB) methods provide a computationally appealing means to infer the posterior density of unkn...
| Главные авторы: | , , , |
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| Другие авторы: | |
| Формат: | Статья |
| Язык: | en_US |
| Опубликовано: |
Institute of Electrical and Electronics Engineers
2012
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| Online-ссылка: | http://hdl.handle.net/1721.1/70598 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0001-7149-3584 |