Optimal average of regional temperature with sampling error estimation
نویسندگان
چکیده
منابع مشابه
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation of the expected future error is intractable. Our approach is made feasible by taking a sampling approach to estima...
متن کاملOptimal Sampling for Parameters Estimation
In the problems concerning prediction and modeling, parameters estimation constitutes one of the main uncertain items that must be taken into account. The easiest way to minimize this uncertainty is to collect great amounts of data. The aim of this work is to build a decision model able to choose the optimal position of the sample point used for the parameters estimation, minimizing the paramet...
متن کاملProgressive Sampling for Association Rules Based on Sampling Error Estimation
We explore in this paper a progressive sampling algorithm, called Sampling Error Estimation (SEE), which aims to identify an appropriate sample size for mining association rules. SEE has two advantages over previous works in the literature. First, SEE is highly efficient because an appropriate sample size can be determined without the need of executing association rules. Second, the identified ...
متن کاملOptimal detection of M-QAM signal with channel estimation error
In this paper, we study the optimal detection of QAM signaling with channel estimation error. Optimal detectors, Maximum Likelihood (MaxLike) detector and a Modified Minimum Distance (ModDist) detector, are derived for the case of a known Gaussian channel distribution and unknown channel distribution, respectively. These detectors differ from the traditional minimum distance detector which igno...
متن کاملFast optimal CMB power spectrum estimation with Hamiltonian sampling
We present a method for fast optimal estimation of the temperature angular power spectrum from observations of the cosmic microwave background. We employ a Hamiltonian Monte Carlo (HMC) sampler to obtain samples from the posterior probability distribution of all the power spectrum coefficients given a set of observations. We compare the properties of the HMC and the related Gibbs sampling appro...
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: Atmosphere-Ocean
سال: 1997
ISSN: 0705-5900,1480-9214
DOI: 10.1080/07055900.1997.9649589