Stellar atmospheric parameter estimation using Gaussian process regression
نویسندگان
چکیده
منابع مشابه
Estimation of Clustering Parameters Using Gaussian Process Regression
We propose a method for estimating the clustering parameters in a Neyman-Scott Poisson process using Gaussian process regression. It is assumed that the underlying process has been observed within a number of quadrats, and from this sparse information the distribution is modelled as a Gaussian process. The clustering parameters are then estimated numerically by fitting to the covariance structu...
متن کاملGaussian Process Quantile Regression using Expectation Propagation
Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables. We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function. The integration required in learning is not analytically tractable so to speed up the learning we employ the Expectation Propagat...
متن کاملFast Gaussian Process Regression using KD-Trees
The computation required for Gaussian process regression with n training examples is about O(n) during training and O(n) for each prediction. This makes Gaussian process regression too slow for large datasets. In this paper, we present a fast approximation method, based on kd-trees, that significantly reduces both the prediction and the training times of Gaussian process regression.
متن کاملMonthly streamflow forecasting using Gaussian Process Regression
Bureau of Economic Geology, Jackson School of Geosciences, University of Texas Austin, Austin, TX 78713, United States Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, United States Key Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical, Agriculture, Chinese Academy of Sciences, Changsha, Ch...
متن کاملParameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation
Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...
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: Monthly Notices of the Royal Astronomical Society
سال: 2014
ISSN: 0035-8711,1365-2966
DOI: 10.1093/mnras/stu2063