Efficient Gaussian process regression for large datasets
نویسندگان
چکیده
منابع مشابه
Efficient Gaussian process regression for large datasets.
Gaussian processes are widely used in nonparametric regression, classification and spatiotemporal modelling, facilitated in part by a rich literature on their theoretical properties. However, one of their practical limitations is expensive computation, typically on the order of n3 where n is the number of data points, in performing the necessary matrix inversions. For large datasets, storage an...
متن کاملEfficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes
This paper develops an efficient computational method for solving a Gaussian process (GP) regression for large spatial data sets using a collection of suitably defined local GP regressions. The conventional local GP approach first partitions a domain into multiple non-overlapping local regions, and then fits an independent GP regression for each local region using the training data belonging to...
متن کاملAdaptive Gaussian Predictive Process Models for Large Spatial Datasets.
Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is det...
متن کاملEfficient Variational Inference for Gaussian Process Regression Networks
In multi-output regression applications the correlations between the response variables may vary with the input space and can be highly non-linear. Gaussian process regression networks (GPRNs) are flexible and effective models to represent such complex adaptive output dependencies. However, inference in GPRNs is intractable. In this paper we propose two efficient variational inference methods f...
متن کاملEfficient Optimization for Sparse Gaussian Process Regression: Supplementary Material
K is the rank n full covariance matrix to be factorized, and K does not need to precomputed (taking up O(n) storage), but just need to return its diagonal and specific column when queried (a function handle for example). If σ is supplied, the algorithm below operates with an additional twist allowing the augmentation trick introduced in Sec. 3 of the paper, in which case the matrix L in the alg...
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: Biometrika
سال: 2012
ISSN: 0006-3444,1464-3510
DOI: 10.1093/biomet/ass068