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2009 NIPS NeurIPS 2009

Time-Varying Dynamic Bayesian Networks

Abstract

Directed graphical models such as Bayesian networks are a favored formalism to model the dependency structures in complex multivariate systems such as those encountered in biology and neural sciences. When the system is undergoing dynamic transformation, often a temporally rewiring network is needed for capturing the dynamic causal influences between covariates. In this paper, we propose a time-varying dynamic Bayesian network (TV-DBN) for modeling the structurally varying directed dependency structures underlying non-stationary biological/neural time series. This is a challenging problem due the non-stationarity and sample scarcity of the time series. We present a kernel reweighted $\ell_1$ regularized auto-regressive procedure for learning the TV-DBN model. Our method enjoys nice properties such as computational efficiency and provable asymptotic consistency. Applying TV-DBN to time series measurements during yeast cell cycle and brain response to visual stimuli reveals interesting dynamics underlying the respective biological systems.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Data Science & Analytics and Knowledge & Reasoning
📈 Trend Setter — Causal Inference
🧭 Keyword Pioneer — non-stationary time series
🐣 Hot Topic Early Bird — causal inference
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio

Authors

Le Song , Mladen Kolar , Eric P. Xing

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Knowledge & Reasoning > Reasoning > Causal Inference Data Science & Analytics > Methods > Time Series Analysis Machine Learning > Core Methods > Graphical Models Machine Learning > Bayesian & Probabilistic > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Bayesian Networks Machine Learning > Core Methods > Sequence Modeling Interdisciplinary > Science > Bioinformatics

Keywords

time series analysis causal inference time series sparse regression kernel regularization non-stationary time series kernel reweighted regularization graphical model auto-regressive model dynamic bayesian network biological system biological time series
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