An efficient aircraft conflict probability estimation method using a spatial multi-resolution scheme

2021 ◽  
Author(s):  
Seyedhamed Seyedipour ◽  
Hadi Nobahari ◽  
Maria Prandini
Keyword(s):  
Estimation Method ◽  
Probability Estimation ◽  
Resolution Scheme

Statistical estimation of container condensation in marine transportation between Far East Asia and Europe

2021 ◽  
pp. 1-24
Author(s):  
Ping Chi Yuen ◽  
Kenji Sasa ◽  
Hideo Kawahara ◽  
Chen Chen
Keyword(s):  
Statistical Estimation ◽  
Far East ◽  
Estimation Method ◽  
Measured Data ◽  
Probability Estimation ◽  
Container Transportation ◽  
Marine Transportation ◽  
Weather Changes ◽  
Sea Area ◽  
Weather Change

Abstract Condensation inside marine containers occurs during voyages owing to weather changes. In this study, we define the condensation probability along one of the major routes for container ships between Asia and Europe. First, the inside and outside air conditions were measured on land in Japan, and a correlation analysis was conducted to derive their relationship. Second, onboard measurements were conducted for 20,000 twenty-foot equivalent unit (TEU) ships to determine the variation in outside air conditions. Complicated patterns of weather change were observed with changes in latitude, sea area, and season. Third, condensation probability was estimated based on a multi-regression analysis with land and onboard measured data. The maximum condensation probability in westbound or eastbound voyages in winter was found to be approximately 50%. The condensation probability estimation method established in this study can contribute to the quantification of cargo damage risks for the planning of marine container transportation voyages.

scholarly journals Probability Estimation in the Framework of Intuitionistic Fuzzy Evidence Theory

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yafei Song ◽  
Xiaodan Wang
Keyword(s):  
Evidence Theory ◽  
Estimation Method ◽  
Probability Estimation ◽  
Belief Functions ◽  
Intuitionistic Fuzzy ◽  
Dempster Shafer Theory ◽  
Fuzzy Environment ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Interval Valued

Intuitionistic fuzzy (IF) evidence theory, as an extension of Dempster-Shafer theory of evidence to the intuitionistic fuzzy environment, is exploited to process imprecise and vague information. Since its inception, much interest has been concentrated on IF evidence theory. Many works on the belief functions in IF information systems have appeared. Although belief functions on the IF sets can deal with uncertainty and vagueness well, it is not convenient for decision making. This paper addresses the issue of probability estimation in the framework of IF evidence theory with the hope of making rational decision. Background knowledge about evidence theory, fuzzy set, and IF set is firstly reviewed, followed by introduction of IF evidence theory. Axiomatic properties of probability distribution are then proposed to assist our interpretation. Finally, probability estimations based on fuzzy and IF belief functions together with their proofs are presented. It is verified that the probability estimation method based on IF belief functions is also potentially applicable to classical evidence theory and fuzzy evidence theory. Moreover, IF belief functions can be combined in a convenient way once they are transformed to interval-valued possibilities.

scholarly journals Revisiting the Optimal Probability Estimator from Small Samples for Data Mining

2019 ◽  
Vol 29 (4) ◽  
pp. 783-796 ◽  
Author(s):  
Bojan Cestnik
Keyword(s):  
Estimation Method ◽  
Absolute Error ◽  
Estimation Methods ◽  
Probability Estimation ◽  
Small Samples ◽  
Estimation Errors ◽  
Advantages And Disadvantages ◽  
Discovery Research ◽  
Single Instance ◽  
Depth Analysis

Abstract Estimation of probabilities from empirical data samples has drawn close attention in the scientific community and has been identified as a crucial phase in many machine learning and knowledge discovery research projects and applications. In addition to trivial and straightforward estimation with relative frequency, more elaborated probability estimation methods from small samples were proposed and applied in practice (e.g., Laplace’s rule, the m-estimate). Piegat and Landowski (2012) proposed a novel probability estimation method from small samples Eph√2 that is optimal according to the mean absolute error of the estimation result. In this paper we show that, even though the articulation of Piegat’s formula seems different, it is in fact a special case of the m-estimate, where pa =1/2 and m = √2. In the context of an experimental framework, we present an in-depth analysis of several probability estimation methods with respect to their mean absolute errors and demonstrate their potential advantages and disadvantages. We extend the analysis from single instance samples to samples with a moderate number of instances. We define small samples for the purpose of estimating probabilities as samples containing either less than four successes or less than four failures and justify the definition by analysing probability estimation errors on various sample sizes.

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