Alternative renormalizable SO(10) GUTs and data fitting
نویسندگان
چکیده
منابع مشابه
Exponential data fitting
In this initial chapter we consider some of the basic methods used in the fitting of data by real and complex linear combinations of exponentials. We have selected the classes of methods that are most frequently used in many different fields: variable projections for solving this separable nonlinear least squares problem, derivatives and variants of Prony’s method, which rely on evenly sampled ...
متن کاملGeometric Data Fitting
Given a dense set of points lying on or near an embedded submanifold M0 ⊂ Rn of Euclidean space, the manifold fitting problem is to find an embedding F :M → Rn that approximatesM0 in the sense of least squares. When the dataset is modeled by a probability distribution, the fitting problem reduces to that of finding an embedding that minimizes Ed[F], the expected square of the distance from a po...
متن کاملMultiple Neutral Data Fitting
A method is proposed for estimating the relationship between a number of variables; this differs from regression where the emphasis is on predicting one of the variables. Regression assumes that only one of the variables has error or natural variability, whereas our technique does not make this assumption; instead, it treats all variables in the same way and produces models which are units inva...
متن کاملAlternative ranking method in Dynamic Data Envelopment Analysis (DDEA)
The motivation of this paper is to propose such equitable method for ranking all decision making units (DMUs) in dynamic Data Envelopment Analysis (DDEA) framework. As far as we are aware there is not more studies in dynamic DEA literature. What's more, in such cases the best operating unit is important to be sampled for the others in under evaluated time periods. However, in this special conce...
متن کاملFitting Uncertain Data with NURBS
Fitting of uncertain data, that is, fitting of data points that are subject to some error, has important applications for example in statistics and for the evaluation of results from physical experiments. Fitting in these problem domains is usually achieved with polynomial approximation, which involves the minimization of an error at discrete data points. Norms typically used for this minimizat...
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: Nuclear Physics B
سال: 2020
ISSN: 0550-3213
DOI: 10.1016/j.nuclphysb.2020.114992