Sequential Inference for Gaussian Processes: A Signal Processing Perspective
The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. ML models support the development of SP systems that represent complex, nonlinear relationships with high predictive accuracy. Adapting these models often requires sequential inference, which differs both theoretically and methodologically from the usual paradigm of ML, where data are often assumed independent and identically distributed. Gaussian processes (GPs) are a flexible yet principled framework for modeling random functions, and they have become increasingly relevant to SP as statistical and ML methods as...
| Authors | Daniel Waxman, Fernando Llorente, Petar M. Djurić |
|---|---|
| Primary category | eess.SP |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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