2021/01/31 by Sangwon Lee, Vipul Periwal, Junghyo Jo · 5 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Neuroscience · Physics and Astronomy · #Algorithm #Artificial intelligence #Computer science #Consistency (knowledge bases) #Data consistency #Data mining #Data point #Expectation–maximization algorithm #Functional Brain Connectivity Studies #Gene Regulatory Network Analysis #Inference #Iterated function #Machine learning #Mathematics #Maximum likelihood #Missing data #Neural dynamics and brain function #Series (stratigraphy) #Statistical inference #Statistics #Synthetic data #Time series #physics.data-an #stat.ML
paper · pdf · doi:10.1103/physreve.104.024119
published in Physical review. E 104(2), 024119 (American Physical Society)
arxiv created 2021/07/16 · openalex publication_date 2021/08/16 · arxiv updated 2021/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Inferring dynamics from time series is an important objective in data analysis. In particular, it is challenging to infer stochastic dynamics given incomplete data. We propose an expectation maximization (EM) algorithm that iterates between alternating two steps: E-step restores missing data points, while M-step infers an underlying network model from the restored data. Using synthetic data of a kinetic Ising model, we confirm that the algorithm works for restoring missing data points as well as inferring the underlying model. At the initial iteration of the EM algorithm, the model inference shows better model-data consistency with observed data points than with missing data points. As we keep iterating, however, missing data points show better model-data consistency. We find that demanding equal consistency of observed and missing data points provides an effective stopping criterion for the iteration to prevent going beyond the most accurate model inference. Using the EM algorithm and the stopping criterion together, we infer missing data points from a time-series data of real neuronal activities. Our method reproduces collective properties of neuronal activities such as correlations and firing statistics even when 70% of data points are masked as missing points.