Results 51 to 60 of about 731,480 (301)
A Bayesian Binomial Regression Model with Latent Gaussian Processes for Modelling DNA Methylation
Austrian Journal of Statistics, 2020 Epigenetic observations are represented by the total number of reads from a given pool of cells and the number of methylated reads, making it reasonable to model this data by a binomial distribution.
Aliaksandr Hubin +3 more
doaj +1 more source
Convergence rates of posterior distributions for noniid observations [PDF]
, 2007 We consider the asymptotic behavior of posterior distributions and Bayes estimators based on observations which are required to be neither independent nor identically distributed.
Ghosal, Subhashis, van der Vaart, Aad
core +5 more sources
Dynamic Shrinkage Processes [PDF]
, 2018 We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building upon a global-local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both ...
Abramovich +44 more
core +2 more sources
Massively Parallel Approximate Gaussian Process Regression [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2014 24 pages, 6 figures, 1 ...
Gramacy, Robert +2 more
openaire +3 more sources
Locally Smoothed Gaussian Process Regression
Procedia Computer Science, 2022 We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation.
Gogolashvili, Davit +2 more
openaire +2 more sources
Gaussian Process Regression Networks [PDF]
, 2011 17 pages, 3 figures, 1 table.
Andrew Gordon Wilson +2 more
openaire +2 more sources
Efficient Gaussian process regression for large datasets [PDF]
Biometrika, 2012 Gaussian processes (GPs) are widely used in nonparametric regression, classification and spatio-temporal modeling, motivated in part by a rich literature on theoretical properties. However, a well known drawback of GPs that limits their use is the expensive computation, typically O($n^3$) in performing the necessary matrix inversions with $n$ denoting ...
Banerjee, Anjishnu +2 more
openaire +4 more sources
Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression
UniverseThe current accelerated expansion of the Universe remains one of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application
José de Jesús Velázquez +3 more
doaj +1 more source
Complex Gaussian Processes for Regression [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2015 In this paper, we propose a novel Bayesian solution for nonlinear regression in complex fields. Previous solutions for kernels methods usually assume a complexification approach, where the real-valued kernel is replaced by a complex-valued one.
R. Boloix-Tortosa +3 more
semanticscholar +1 more source
Decomposed Gaussian Processes for Efficient Regression Models with Low Complexity
EntropyIn this paper, we address the challenges of inferring and learning from a substantial number of observations (N≫1) with a Gaussian process regression model.
Anis Fradi, Tien-Tam Tran, Chafik Samir
doaj +1 more source