This data article provides the summary data from tests comparing various Gaussian process software packages. Each spreadsheet represents a single function or type of function using a particular input sample size. In each spreadsheet, a row gives the results for a particular replication using a single package. Within each spreadsheet there are the results from eight Gaussian process model-fitting packages on five replicates of the surface. There is also one spreadsheet comparing the results from two packages performing stochastic kriging. These data enable comparisons between the packages to determine which package will give users the best results.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995745 | PMC |
| http://dx.doi.org/10.1016/j.dib.2017.12.012 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning.
Front Plant Sci
January 2025
College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China.
Chlorophyll density (ChD) can reflect the photosynthetic capacity of the winter wheat population, therefore achieving real-time non-destructive monitoring of ChD in winter wheat is of great significance for evaluating the growth status of winter wheat. Derivative preprocessing has a wide range of applications in the hyperspectral monitoring of winter wheat chlorophyll. In order to research the role of fractional-order derivative (FOD) in the hyperspectral monitoring model of ChD, this study based on an irrigation experiment of winter wheat to obtain ChD and canopy hyperspectral reflectance.
View Article and Find Full Text PDFUnderstanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which describe high-dimensional neural time series in terms of low-dimensional latent dynamics have played a fundamental role in characterizing neural systems. Yet, what constitutes a successful method involves two opposing criteria: (1) methods should be expressive enough to capture complex nonlinear dynamics, and (2) they should maintain a notion of interpretability often only warranted by simpler linear models.
View Article and Find Full Text PDFGaussian Process Approach to Constructing Transferable Force Fields for Thiolate-Protected Gold Nanoclusters.
J Chem Inf Model
January 2025
Department of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States.
Gold nanoparticles can exhibit unique physical and chemical properties, such as plasmon resonances or photoluminescence. These nanoparticles have many atoms, which leads to high computational costs for density functional theory (DFT) calculations. In this work, we used the FLARE++ (fast learning of atomistic rare events) code and incorporated an active learning algorithm to construct force fields for gold thiolate-protected nanoclusters.
View Article and Find Full Text PDFQuantum state transfer with measurement-based noiseless linear amplification.
Opt Express
December 2024
A partially disembodied quantum state transfer (PDQST) machine is crucial for Gaussian quantum information processing. It integrates Gaussian quantum teleportation and Gaussian quantum cloning machines into a single quantum information platform and therefore enhances the multifunctionality and compatibility of the present quantum information system. However, the standard PDQST machine relies on infinite quantum squeezing, which is unachievable in the practical implementation.
View Article and Find Full Text PDFInvestigating time-independent and time-dependent diffusion phenomena using steady-state diffusion MRI.
Sci Rep
January 2025
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Diffusion MRI is a leading method to non-invasively characterise brain tissue microstructure across multiple domains and scales. Diffusion-weighted steady-state free precession (DW-SSFP) is an established imaging sequence for post-mortem MRI, addressing the challenging imaging environment of fixed tissue with short T and low diffusivities. However, a current limitation of DW-SSFP is signal interpretation: it is not clear what diffusion 'regime' the sequence probes and therefore its potential to characterise tissue microstructure.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!