Transformations of Gaussian Process Priors

39 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Modelling transcriptional regulation using Gaussian Processes (2006, methodology): “Gaussian processes have been used effectively in a number of machine learning and statistical applications [8] (see also [2, 6] for the work that is most closely related to ours).”
Dynamic systems identification with Gaussian processes (2005, background): “Dynamic systems identiﬁcation with GPs ‘derivative observations’ taken from local linearizations in [8], and our work on transformations of GPs [20] allows the user to design appropriate ﬁlters or to cope with sensor dynamics.”
Bayesian Gaussian processes for sequential prediction, optimisation and quadrature (2010, background): “7 Derivative and Integral Observations We now highlight a very useful property of a GP [Murray-Smith and Pearlmutter, 2005].”
Gaussian Process Dynamical Models (2005, background): “” Higher-order features can also be fused together with position information to reduce the Gaussian process prediction variance [15, 9].”
Gaussian Processes for time-marked time-series data (2012, background): “While other non-parametric Gaussian Processes have been studied (Sampson and Guttorp, 1992; Nott and Dunsmuir, 2002; Schmidt and O’Hagan, 2003; Murray-Smith and Pearlmutter, 2003; Paciorek and Schervish, 2006, 2003), we consider in depth the particular case of causal GPs.”