Uncertain Statistics and COVID-19 Spread in China

Uncertain statistics is a set of mathematical techniques for collecting, analyzing and interpreting data by uncertainty theory. In this paper, the main topics of uncertain statistics, including estimation of uncertainty distribution, uncertain regression analysis, uncertain times series, uncertain differential equation and uncertain hypothesis test, are reviewed. Furthermore, by the application to the COVID-19 spread in China, the advantages of those techniques in uncertain statistics are sorted out.

Pythagorean m -Polar Fuzzy Weighted Aggregation Operators and Algorithm for the Investment Strategic Decision Making

The role of multipolar uncertain statistics cannot be unheeded while confronting daily life problems on well-founded basis. Fusion (aggregation) of a number of input values in multipolar form into a sole multipolar output value is an essential tool not merely of physics or mathematics but also of widely held problems of economics, commerce and trade, engineering, social sciences, decision-making problems, life sciences, and many more. The problem of aggregation is very wide-ranging and fascinating, in general. We use, in this article, Pythagorean fuzzy numbers (PFNs) in multipolar form to contrive imprecise information. We introduce Pythagorean m -polar fuzzy weighted averaging (P m FWA), Pythagorean m -polar fuzzy weighted geometric (P m FWG), symmetric Pythagorean m -polar fuzzy weighted averaging (SP m FWA), and symmetric Pythagorean m -polar fuzzy weighted geometric (SP m FWG) operators for aggregating uncertain data. Finally, we present a practical example to illustrate the application of the proposed operators and to demonstrate its practicality and effectiveness towards investment strategic decision making.

Optimization Model for Uncertain Statistics Based on an Analytic Hierarchy Process

Uncertain statistics is a methodology for collecting and interpreting the expert’s experimental data by uncertainty theory. In order to estimate uncertainty distributions, an optimization model based on analytic hierarchy process (AHP) and interpolation method is proposed in this paper. In addition, the principle of least squares method is presented to estimate uncertainty distributions with known functional form. Finally, the effectiveness of this method is illustrated by an example.

Constructive Filtering Algorithms for Delayed Systems With Uncertain Statistics

A stochastic optimal guaranteed estimation problem for dynamic delayed systems with uncertain statistics is considered. The solution of this problem reduces to a complex nonsmooth extremal problem. To obtain an approximate solution, the nonsmooth problem is replaced by a smooth one. Constructive filtering algorithms are obtained from an approximate solution of the smooth problem under the assumption that the delay is small in comparison with the observation time. Estimates for the nonoptimality levels of the proposed filtering algorithms are derived.