Unifying Bayesian Nonparametric Methods and Variational Inference for Robust Model Adaptation in Non-Stationary Data Environments
Keywords:
Bayesian nonparametrics, Variational inference, Non-stationary data, Model adaptation, Probabilistic machine learningAbstract
Non-stationary data environments present significant challenges for statistical modeling and machine learning, requiring adaptive techniques that can generalize across shifting distributions. Bayesian nonparametric (BNP) methods provide flexibility in handling infinite-dimensional model spaces, while variational inference (VI) ensures computational efficiency in high-dimensional settings. This paper explores the unification of BNP and VI to develop robust adaptive models capable of learning in dynamically evolving environments. We discuss recent advancements in 2023, provide a comparative analysis of existing techniques, and introduce experimental results showcasing the benefits of our proposed approach. The findings suggest that integrating BNP with VI offers superior performance in handling non-stationarity, making it a viable solution for real-world applications such as finance, healthcare, and autonomous systems.
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