Gaussian Process Models for Non Parametric Functional Regression with Functional Responses
نویسندگان
چکیده
منابع مشابه
Parametric Gaussian Process Regression for Big Data
This work introduces the concept of parametric Gaussian processes (PGPs), which is built upon the seemingly self-contradictory idea of making Gaussian processes parametric. Parametric Gaussian processes, by construction, are designed to operate in “big data” regimes where one is interested in quantifying the uncertainty associated with noisy data. The proposed methodology circumvents the welles...
متن کاملLearning Non-Parametric Prediction and Observation Models for Bayesian Filtering via Gaussian Process Regression
Bayesian filtering is a general framework for estimating the state of a dynamical system [3]. Bayes filters recursively estimate posterior distributions over the state of a dynamical system conditioned on sensor information collected so far. Bayes filter updates can be broken into two steps. The prediction step uses a probabilistic model, or prediction model, of the system dynamics to propagate...
متن کاملGaussian process functional regression modeling for batch data.
A Gaussian process functional regression model is proposed for the analysis of batch data. Covariance structure and mean structure are considered simultaneously, with the covariance structure modeled by a Gaussian process regression model and the mean structure modeled by a functional regression model. The model allows the inclusion of covariates in both the covariance structure and the mean st...
متن کاملFunctional Coefficient Nonstationary Regression with Non– and Semi–Parametric Cointegration∗
This paper studies a general class of nonlinear varying coefficient time series models with possible nonstationarity in both the regressors and the varying coefficient components. The model accommodates a cointegrating structure and allows for endogeneity with contemporaneous correlation among the regressors, the varying coefficient drivers, and the residuals. This framework allows for a mixtur...
متن کاملCurve prediction and clustering with mixtures of Gaussian process functional regression models
Shi et al. (2006) proposed a Gaussian process functional regression (GPFR) model to model functional response curves with a set of functional covariates. Two main problems are addressed by this method: modelling nonlinear and nonparametric regression relationship and modelling covariance structure and mean structure simultaneously. The method gives very good results for curve fitting and predic...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications in Statistics - Theory and Methods
سال: 2015
ISSN: 0361-0926,1532-415X
DOI: 10.1080/03610926.2013.847101