O'HAGAN, A. 1978. Curve Fitting and Optimal Design for Prediction. J. of the Royal Statistical Society, ser. B 40, 1--42. Home/Search Document Not in Database Summary Related Articles Check |
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Gaussian Process Priors with Uncertain Inputs -.. - Girard.. (2003) (3 citations) (Correct)
....horizon, as well as the joint probability distribution of the predicted points. In the Gaussian process modelling approach, one computes predictive distributions whose means serve as output estimates. Gaussian processes (GPs) for regression have historically been first introduced by O Hagan [1] but started being a popular non parametric modelling approach after the publication of [7] In [10] it is shown that GPs can achieve a predictive performance comparable to (if not better than) other modelling approaches like neural networks or local learning methods. We will show that for a ....
O'Hagan, A. (1978) Curve fitting and optimal design for prediction. Journal of the Royal Statistical Society B 40:1-42.
Multiple-step ahead prediction for non linear dynamic systems - .. - Girard, al. (2003) (1 citation) (Correct)
....points, and generally does not need as large a training set as the direct approach. In the Gaussian process modelling approach, one computes predictive distributions whose means serve as output estimates. Gaussian processes (GPs) for regression have historically been first introduced by O Hagan [1] but started being a popular non parametric modelling approach after the publication of [5] In [6] it is shown that GPs can achieve a predictive performance comparable to (if not better than) other modelling approaches like neural networks or local learning methods. We will show that for a ....
O'Hagan, A. (1978) Curve fitting and optimal design for prediction. Journal of the Royal Statistical Society B 40:1-42.
Global Optimization Approach to Transistor Sizing for.. - Mehrotra, Franzon, Liu (1993) (Correct)
....approximation is inappropriate; information is lost in fitting the model to data, unless the degree of the polynomial is as large as the data set. A better approach to function approximation is needed. Recently, stochastic models have been proposed to capture complex objective functions [14]. With this approach, the value of the unknown function at each point in A is assumed to be a random variable. Then, the unknown function itself is a sample path of a stochastic function. In the general case, a stochastic function qS(a) is defined by a family of multidi mensional probability ....
....to be discriminate between points of small mean but large vari ance or points of small variance but somewhat larger mean. We shall briefly review some proposed algorithms that fit the stochastic modeling paradigm. For a good discussion on stochastic modeling of unknown functions, see reference [14]. Global optimization methods are further described by Mockus in [10] 2.1 Algorithms based on Statistical Modeling Several algorithms for optimization using a stochastic model function have been investigated. We summarize some interesting approaches and finish with the P Algorithm which forms ....
A. O'Hagan. Curve fitting and optimal design for prediction. Journal of the Royal Statisitcal Society, 40(1):pp. 1-42, 1978.
Style-Based Inverse Kinematics - Steven (2004) (6 citations) (Correct)
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O'HAGAN, A. 1978. Curve Fitting and Optimal Design for Prediction. J. of the Royal Statistical Society, ser. B 40, 1--42.
Journal of Machine Learning Research 7 (2006).. - Non-Linear.. (Correct)
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Anthony O'Hagan. Curve fitting and optimal design for prediction. Journal of the Royal Statistical Society, Series B (Methodological), 40(1):1--42, 1978.
Gaussian Processes for Ordinal Regression - Chu, Ghahramani (2005) (1 citation) (Correct)
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A. O'Hagan. Curve fitting and optimal design for prediction (with discussion). Journal of the Royal Statistical Society B, 40(1):1--42, 1978.
Gaussian Process Priors with Uncertain Inputs -.. - Girard, Rasmussen, al. (2003) (3 citations) (Correct)
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O'Hagan, A. (1978) Curve fitting and optimal design for prediction. Journal of the Royal Statistical Society B 40:1-42.
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