Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes
| Autor: | Heinz Koeppl, Sara Al-Sayed |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
state-space representation
Network reconstruction State-space representation Computer science Perturbation (astronomy) 020206 networking & telecommunications Multivariate normal distribution 02 engineering and technology Kalman filter Network dynamics multivariate Gaussian processes symbols.namesake time- course data Time course 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Gaussian process Algorithm |
| Zdroj: | IEEE MLSP 2018 (MLSP 2018) 2018 IEEE International Workshop on Machine Learning for Signal Processing MLSP |
| DOI: | 10.5281/zenodo.1488636 |
| Popis: | In this work, we appropriate the popular tool of Gaussian processes to solve the problem of reconstructing networks from time-series perturbation data. To this end, we propose a construction for multivariateGaussian processes to describe the continuous-time trajectories of the states of the network entities. We then show that this construction admits a state-space representation for the network dynamics.By exploiting Kalman filtering techniques, we are able to infer the underlying network in a computationally efficient manner. |
| Databáze: | OpenAIRE |
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