Deep state-space Gaussian processes
نویسندگان
چکیده
This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed (GP) priors on length scales and magnitudes of next level processes in hierarchy. The idea represent as non-linear hierarchical system linear stochastic differential equations (SDEs), where each SDE corresponds conditional GP. regression problem then becomes state estimation problem, we can estimate efficiently sequential methods using Markov property DGP. computational complexity linearly respect number measurements. Based this, formulate MAP well Bayesian filtering smoothing solutions problem. demonstrate performance proposed models synthetic non-stationary signals apply detection gravitational waves from LIGO
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ژورنال
عنوان ژورنال: Statistics and Computing
سال: 2021
ISSN: ['0960-3174', '1573-1375']
DOI: https://doi.org/10.1007/s11222-021-10050-6