Heteroscedastic Gaussian process regression
Explore this paper's citation graph
Summary
An algorithm to estimate simultaneously both mean and variance of a non parametric regression problem which can be solved via Newton's method is presented and is able to estimate variance locally unlike standard Gaussian Process regression or SVMs.
- Type
- article
- Published
- 2005-08-07
- Cited by
- 252
- References
- 10
- OpenAlex
- https://openalex.org/W2055825262
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2891780
Keywords
Heteroscedasticity, Estimator, Computer science, Gaussian process, Kriging
References
- Approximately unbiased estimation of conditional variance in heteroscedastic kernel ridge regression
- CBMS-NSF REGIONAL CONFERENCE SERIES IN APPLIED MATHEMATICS
- Doubly penalized likelihood estimator in heteroscedastic regression
- Efficient SVM Training Using Low-Rank Kernel Representations
- New Support Vector Algorithms
- Regression with Input-dependent Noise: A Gaussian Process Treatment
- Spline Models for Observational Data
- Exponential Families for Conditional Random Fields
- Exponential families for conditional random fields
- Spline Models for Observational Data.
- Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond
- Learning the Kernel Matrix with Semidefinite Programming
Cited by
- Virtual metrology for plasma etch processes.
- A probablistic framework for classification and fusion of remotely sensed hyperspectral data
- Using Gaussian Processes for the Calibration and Exploration of Complex Computer Models
- Sensor-based feedback for piano pedagogy
- Data fusion with Gaussian processes
- Divisive Gaussian Processes for Nonstationary Regression
- Heteroscedastic Gaussian process regression using expectation propagation
- GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
- A Mine on Its Own
- New support vector algorithms with parametric insensitive/margin model
- Heteroscedastic Gaussian processes for data fusion in large scale terrain modeling
- Expectation propagation for nonstationary heteroscedastic Gaussian process regression
- Learning gas distribution models using sparse Gaussian process mixtures
- Kernel methods and the exponential family
- Information fusion in multi-task Gaussian processes
- Gaussian Process Training with Input Noise
- GP-UKF: Unscented kalman filters with Gaussian process prediction and observation models
- Gaussian process product models for nonparametric nonstationarity
- Nonparametric Bayesian inference on multivariate exponential families
- Most likely heteroscedastic Gaussian process regression
Related papers
- Estimation of the Variance Function in Heteroscedastic Linear Regression Models
- Heteroscedasticity check in nonlinear semiparametric models based on nonparametric variance function
- Statistical Analysis of Heteroscedasticity in Nonlinear Regression Models with Random Weight Function
- Improving weighted least-squares estimates in heteroscedastic linear regression when the variance is a function of the mean response
- Effectiveness of robust methods in heterogeneous linear models
- Impact of variance function estimation in regression and calibration.
- Robust Wild Bootstrap for Stabilizing the Variance of Parameter Estimates in Heteroscedastic Regression Models in the Presence of Outliers
- Statistical inference of partially linear regression models with heteroscedastic errors