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Sparse Approximate Inference for Spatio-Temporal Point Process Models
| Content Provider | Semantic Scholar |
|---|---|
| Author | Cseke, Botond Zammit-Mangion, Andrew Heskes, Tom Sanguinetti, Guido |
| Copyright Year | 2013 |
| Abstract | ABSTRACTSpatio-temporal log-Gaussian Cox process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computationally challenging both due to the high-resolution modeling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of (spatially) discretized log-Gaussian Cox process models by using approximate message-passing algorithms. The proposed algorithms scale well with the state dimension and the length of the temporal horizon with moderate loss in distributional accuracy. They hence provide a flexible and faster alternative to both nonlinear filtering-smoothing type algorithms and to approaches that implement the Laplace method or expectation propagation on (block) sparse latent Gaussian models. We infer the parameters of the latent Gaussian model using a structured variational Bayes approach. We demonstrate the proposed framework on simulation studies with bo... |
| Starting Page | 1746 |
| Ending Page | 1763 |
| Page Count | 18 |
| File Format | PDF HTM / HTML |
| DOI | 10.1080/01621459.2015.1115357 |
| Volume Number | 111 |
| Alternate Webpage(s) | https://repository.ubn.ru.nl/bitstream/handle/2066/163212/163212.pdf?sequence=1 |
| Alternate Webpage(s) | http://ims-vilnius2018.com/content/pdf/ivc236.pdf |
| Alternate Webpage(s) | http://repository.ubn.ru.nl/bitstream/handle/2066/163212/163212.pdf?sequence=1 |
| Alternate Webpage(s) | https://doi.org/10.1080/01621459.2015.1115357 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
