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This article presents a novel method for learning time‐varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time‐varying network structure, and naturally handles multi‐subject data. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by extensive numerical experiments. To showcase its effectiveness, we apply the proposed method to an fMRI study investigating the effective connectivity among various regions of interest (ROIs) during an emotion‐processing task. Our findings reveal the pivotal role of the subcortical‐cerebellum in emotion processing. 

High‐dimensional inference is one of fundamental problems in modern biomedical studies. However, the existing methods do not perform satisfactorily. Based on the Markov property of graphical models and the likelihood ratio test, this article provides a simple justification for the Markov neighborhood regression method such that it can be applied to statistical inference for high‐dimensional generalized linear models with mixed features. The Markov neighborhood regression method is highly attractive in that it breaks the high‐dimensional inference problems into a series of low‐dimensional inference problems. The proposed method is applied to the cancer cell line encyclopedia data for identification of the genes and mutations that are sensitive to the response of anti‐cancer drugs. The numerical results favor the Markov neighborhood regression method to the existing ones.