期刊
PHYSICAL REVIEW E
卷 99, 期 4, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevE.99.042114
关键词
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资金
- Intramural Research Program of the National Institutes of Health, NIDDK
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2016R1D1A1B03932264]
- NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [ZIADK075091] Funding Source: NIH RePORTER
- National Research Foundation of Korea [2016R1D1A1B03932264] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The explosion of activity in finding interactions in complex systems is driven by availability of copious observations of complex natural systems. However, such systems, e.g., the human brain, are rarely completely observable. Interaction network inference must then contend with hidden variables affecting the behavior of the observed parts of the system. We present an effective approach for model inference with hidden variables. From configurations of observed variables, we identify the observed-to-observed, hidden-to-observed, observedto-hidden, and hidden-to-hidden interactions, the configurations of hidden variables, and the number of hidden variables. We demonstrate the performance of our method by simulating a kinetic Ising model, and show that our method outperforms existing methods. Turning to real data, we infer the hidden nodes in a neuronal network in the salamander retina and a stock market network. We show that predictive modeling with hidden variables is significantly more accurate than that without hidden variables. Finally, an important hidden variable problem is to find the number of clusters in a dataset. We apply our method to classify MNIST handwritten digits. We find that there are about 60 clusters which are roughly equally distributed among the digits.
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