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About this paper
Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen
The authors present a Gaussian Process State-Space Model that infers hidden states and transition dynamics from noisy measurements, then forecasts states. It uses Gaussian state marginals, inducing variables, and Markov structure to optimize a variational bound deterministically, with linear complexity in sequence length. Its ELBO check reports −0.220 and −46.9 for linear and nonlinear regimes, respectively, showing conditional reliability.
Summary by alphaXiv.
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