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Mixtures of Gaussian Process Priors
 In Proceedings of the Ninth International Conference on Artificial Neural Networks (ICANN99), IEEE Conference Publication No. 470. London: Institution of Electrical Engineers
"... Nonparametric Bayesian approaches based on Gaussian processes have recently become popular in the empirical learning community. They encompass many classical methods of statistics, like Radial Basis Functions or various splines, and are technically convenient because Gaussian integrals can be calcul ..."
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Cited by 9 (5 self)
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be calculated analytically. Restricting to Gaussian processes, however, forbids for example the implemention of genuine nonconcave priors. Mixtures of Gaussian process priors, on the other hand, allow the flexible implementation of complex and situation specific, also nonconcave a priori information
Transformations of Gaussian Process Priors
"... Gaussian processesprior systems generally consist of noisy measurements of samples of the putatively Gaussian process of interest, where the samples serve to constrain the posterior estimate. Here we consider the case where the measurements are instead noisy weighted sums of samples. This frame ..."
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Cited by 9 (2 self)
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Gaussian processesprior systems generally consist of noisy measurements of samples of the putatively Gaussian process of interest, where the samples serve to constrain the posterior estimate. Here we consider the case where the measurements are instead noisy weighted sums of samples
Learning a Gaussian Process Prior
 In Advances in Neural Information Processing Systems
, 2001
"... This paper presents AutoDJ: a system for automatically generating music playlists based on one or more seed songs selected by a user. AutoDJ uses Gaussian Process Regression to learn a user preference function over songs. This function takes music metadata as inputs. This paper further introduce ..."
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Cited by 1 (0 self)
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This paper presents AutoDJ: a system for automatically generating music playlists based on one or more seed songs selected by a user. AutoDJ uses Gaussian Process Regression to learn a user preference function over songs. This function takes music metadata as inputs. This paper further
Gaussian Process priors with ARMA noise models
 Irish Signals and Systems Conference
, 2001
"... We extend the standard covariance function used in the Gaussian Process prior nonparametric modelling approach to include correlated (ARMA) noise models. The improvement in performance is illustrated on some simulation examples of data generated by nonlinear static functions corrupted with additive ..."
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Cited by 25 (15 self)
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We extend the standard covariance function used in the Gaussian Process prior nonparametric modelling approach to include correlated (ARMA) noise models. The improvement in performance is illustrated on some simulation examples of data generated by nonlinear static functions corrupted with additive
Bayesian inference with rescaled Gaussian process priors
, 2007
"... We use rescaled Gaussian processes as prior models for functional parameters in nonparametric statistical models. We show how the rate of contraction of the posterior distributions depends on the scaling factor. In particular, we exhibit rescaled Gaussian process priors yielding posteriors that con ..."
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Cited by 18 (4 self)
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We use rescaled Gaussian processes as prior models for functional parameters in nonparametric statistical models. We show how the rate of contraction of the posterior distributions depends on the scaling factor. In particular, we exhibit rescaled Gaussian process priors yielding posteriors
Bayesian inference with rescaled Gaussian process priors Citation for published version (APA): Bayesian inference with rescaled Gaussian process priors
, 2007
"... Abstract: We use rescaled Gaussian processes as prior models for functional parameters in nonparametric statistical models. We show how the rate of contraction of the posterior distributions depends on the scaling factor. In particular, we exhibit rescaled Gaussian process priors yielding posterior ..."
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Abstract: We use rescaled Gaussian processes as prior models for functional parameters in nonparametric statistical models. We show how the rate of contraction of the posterior distributions depends on the scaling factor. In particular, we exhibit rescaled Gaussian process priors yielding
Nonlinear Adaptive Control Using Nonparametric Gaussian Process Prior Models
 IN 15TH IFAC WORLD CONGRESS ON AUTOMATIC CONTROL
, 2002
"... Nonparametric Gaussian Process prior models, taken from Bayesian statistics methodology are used to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads ..."
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Cited by 36 (16 self)
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Nonparametric Gaussian Process prior models, taken from Bayesian statistics methodology are used to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads
Adaptive, cautious, predictive control with Gaussian process priors
 In: IFAC International Symposium on System Identification
, 2003
"... Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a kstep horizon. The expected value of a quadratic cost function is minimised, over this predicti ..."
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Cited by 10 (3 self)
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Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a kstep horizon. The expected value of a quadratic cost function is minimised, over
Regression and Classification Using Gaussian Process Priors
"... Gaussian processes are a natural way of specifying prior distributions over functions of one or more input variables. When such a function defines the mean response in a regression model with Gaussian errors, inference can be done using matrix computations, which are feasible for datasets of up to a ..."
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Cited by 3 (0 self)
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Gaussian processes are a natural way of specifying prior distributions over functions of one or more input variables. When such a function defines the mean response in a regression model with Gaussian errors, inference can be done using matrix computations, which are feasible for datasets of up
Results 1  10
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31,164