Publication: Inference of the kinetic Ising model with heterogeneous missing data
Inference of the kinetic Ising model with heterogeneous missing data
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Campajola, C., Lillo, F., & Tantari, D. (2019). Inference of the kinetic Ising model with heterogeneous missing data. Physical Review. E, 99(6), 062138. https://doi.org/10.1103/physreve.99.062138
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Abstract
We consider the problem of inferring a causality structure from multiple binary time series by using the kinetic Ising model in datasets where a fraction of observations is missing. Inspired by recent work on mean field methods for the inference of the model with hidden spins, we develop a pseudo-expectation-maximization algorithm that is able to work even in conditions of severe data sparsity. The methodology relies on the Martin-Siggia-Rose path integral method with second-order saddle-point solution to make it possible to approxima
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- Lillo, Fabrizio
- Tantari, Daniele
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Campajola, C., Lillo, F., & Tantari, D. (2019). Inference of the kinetic Ising model with heterogeneous missing data. Physical Review. E, 99(6), 062138. https://doi.org/10.1103/physreve.99.062138
