تقرير
Identifying Nonstationary Causal Structures with High-Order Markov Switching Models
| العنوان: | Identifying Nonstationary Causal Structures with High-Order Markov Switching Models |
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| المؤلفون: | Balsells-Rodas, Carles, Wang, Yixin, Mediano, Pedro A. M., Li, Yingzhen |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
| الوصف: | Causal discovery in time series is a rapidly evolving field with a wide variety of applications in other areas such as climate science and neuroscience. Traditional approaches assume a stationary causal graph, which can be adapted to nonstationary time series with time-dependent effects or heterogeneous noise. In this work we address nonstationarity via regime-dependent causal structures. We first establish identifiability for high-order Markov Switching Models, which provide the foundations for identifiable regime-dependent causal discovery. Our empirical studies demonstrate the scalability of our proposed approach for high-order regime-dependent structure estimation, and we illustrate its applicability on brain activity data. Comment: CI4TS Workshop @UAI2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.17698 |
| رقم الأكسشن: | edsarx.2406.17698 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |