Results 131 to 140 of about 346,567 (317)
Gaussian Processes and Fast Matrix-Vector Multiplies [PDF]
, 2009 Gaussian processes (GPs) provide a flexible framework for probabilistic regression. The necessary computations involve standard matrix operations.
Murray, Iain, Murray, Iain; id_orcid
core
Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying
Advanced Engineering Materials, EarlyView.An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara +8 more
wiley +1 more source
Big Data and Cognitive ComputingForecasting of time series data presents some challenges because the data’s nature is complex and therefore difficult to accurately forecast. This study presents the design and development of a novel forecasting system that integrates efficient data ...
Khalil AlSharabi +4 more
doaj +1 more source
Spiked Dirichlet Process Priors for Gaussian Process Models
Journal of Probability and Statistics, 2010 We expand a framework for Bayesian variable selection for Gaussian process (GP) models by employing spiked Dirichlet process (DP) prior constructions over set partitions containing covariates.
Terrance Savitsky, Marina Vannucci
doaj +1 more source
Regularity of gaussian processes
Acta Mathematica, 1987 The author obtains necessary and sufficient conditions for the continuity or boundedness of a Gaussian process. A (centered) Gaussian process is a family \((X_ t)_{t\in T}\) of real-valued random variables, indexed by some index set T, such that every finite linear combination \(\sum a_ tX_ t\) is a real-valued Gaussian random variable.
openaire +3 more sources
Continuous-time Gaussian Process dynamics
, 2023 reservedGaussian processes (GPs) are powerful tools to learn dynamics models that provide also uncertainty estimates for predictions. Most existing approaches are applied to one-step ahead predictions, which could be tempting from a mathematical and ...
TAGLIAPIETRA, NICHOLAS
core
Absolute Moments of Generalized Hyperbolic Distributions and Approximate Scaling of Normal Inverse Gaussian Lévy-Processes [PDF]
, 2004 Expressions for (absolute) moments of generalized hyperbolic (GH) and normal inverse Gaussian (NIG) laws are given in terms of moments of the corresponding symmetric laws.
Barndorff-Nielsen, Ole Eiler +1 more
core +1 more source
In Situ Micromechanical Study of Bimodal γ′–γ″ Precipitate Assemblies in Ni–Cr–Al–Nb Superalloy
Advanced Engineering Materials, EarlyView.A Ni–Cr–Al–Nb superalloy with a bimodal γ′–γ″ precipitate distribution is developed. Composite precipitate assemblies form through heterogeneous nucleation, effectively impeding dislocation motion. Micropillar compression reveals high strength at room and elevated temperatures, governed by precipitate shearing, with coupled faulting mechanisms ...
Ujjval Bansal +4 more
wiley +1 more source
Gaussian-mixture demonstration of the limiting process(es) of stimulus distributions.
, 2022 a) Simple generative model simulated in b-d. x is a scalar drawn from a Gaussian around ±μx (matching the sign of C), and the stimulus s is drawn from a Gaussian around x. b) The prior on x is a mixture of two Gaussians.
Richard D. Lange (11768146) +1 more
core +1 more source
Advanced Engineering Materials, EarlyView.Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán +5 more
wiley +1 more source