Non-linearities in Gaussian processes with integral observations

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Gaussian processes (GP) can be used for inferring latent continuous functions also based on aggregate observations corresponding to integrals of the function, for example to learn daily rate of new infections in a population based on cumulative observations collected only weekly. We extend these approaches to cases where the observations correspond to aggregates of arbitrary non-linear transformations of a GP. Such models are needed, for example, when the latent function of interest is known to be non-negative or bounded. We present a solution based on Markov chain Monte Carlo with numerical integration for aggregation, and demonstrate it in binned Poisson regression and in non-invasive detection of fouling using ultrasound waves.
Titel på gästpublikation 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
Antal sidor6
FörlagIEEE Computer Society
ISBN (tryckt)978-1-7281-6663-6
ISBN (elektroniskt)978-1-7281-6662-9
StatusPublicerad - 2020
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangInternational Workshop on Machine Learning for Signal Processing - Espoo, Finland
Varaktighet: 21 sep 202024 sep 2020
Konferensnummer: 30


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