Statistics with Imprecise Probabilities—A Short Survey

This chapter aims at surveying and highlighting in an introductory way some challenges and big opportunities a paradigmatic shift to imprecise probabilities could induce in statistical modelling. Working with an informal understanding of imprecise probabilities, we discuss the concepts of model imprecision and data imprecision as the two main types of imprecision in statistical modelling. Then we provide a short survey of some major developments, methodological questions and applications of imprecise probabilistic models under model imprecision in the context of different inference schools and summarize some recent developments in the area of data imprecision.

A Systematic Review of Endocrine Therapy for Improved Reproductive and Metabolic Outcomes in PCOS Women

Introduction: Polycystic ovarian syndrome (PCOS) is characterized by infrequent or absent ovulation as well as elevated levels of androgens and insulin (hyperinsulinaemia). The purpose of this study was to determine the efficacy of endocrine treatment in improving reproductive and metabolic outcomes in women with PCOS.Methods: We searched the following databases from inception to Maret 2020: PubMed, Proquest, ScienceDirect, Scopus and CINAHL. We investigated at metformin, clomiphene citrate, metformin plus clomiphene citrate, D-chiro-inositol, statins, and resveratrol as treatments. We compared them to each other, as well as to a placebo or no therapy. The quality of the evidence ranged from extremely low to moderate. The risks of bias (poor reporting of technique and inadequate outcome data), imprecision, and inconsistency were the limitations.Results: Although the evidence quality was low, our latest evaluation indicated that metformin alone may be superior to placebo for live birth. Data for live birth were equivocal when metformin was compared to clomiphene citrate, and our conclusions were hampered by a paucity of evidence. Body mass index (BMI) varies in the results, emphasizing the need of stratifying data by BMI.Conclusion: Clinical pregnancy and ovulation improvements demonstrate that clomiphene citrate is still preferred to metformin for ovulation induction in obese women with PCOS.

Possibilistic Approach for Travel Time Reliability Evaluation

Travel time estimation & reliability evaluation of any means of transportation in every type of travel mode- land, rail, sea and air has been of immense interest of the researchers; primarily due to growing economic concern in the field of logistics & passenger movement. In situations like quantitative data inaccessibility or data imprecision, fuzzy set based possibilistic approach is recognized as a practical choice in obtaining the reliability estimates. This paper proposes and advocates possibilistic approach for travel time reliability computation of any type transportation vehicle under fuzzy type of data. The proposed approach is a novel way of computing the travel time & obtaining the related reliability value. Initially, the paper proposes the general methodology for travel time reliability evaluation. Individual travel time components of a transportation vehicle are considered as fuzzy; as a result, travel time is modelled as a fuzzy variable. Travel time reliability of a transportation vehicle has been defined with the help of possibilistic measures. The proposed procedure is then demonstrated with an application to marine vessel carrying the bulk. After illustration of the proposed methodology, sensitivity analysis is carried out. The paper ends with the comments on comparative features of the three cases.

Generalization Driven Fuzzy Classification Rules Extraction using OLAM Data Cubes

An fuzzy classification rules extraction model for online analytical mining (OLAM) was explained in this article. The efficient integration of the concept of data warehousing, online analytical processing (OLAP) and data mining systems converges to OLAM results in an efficient decision support system. Even after associative classification proved as most efficient classification technique there is a lack of associative classification proposals in field of OLAM. While most of existing data cube models claims their superiority over other the fuzzy multidimensional data cubes proved to be more intuitive in user perspective and effectively manage data imprecision. Considering these factors, in this paper we propose an associative classification model which can perform classification over fuzzy data cubes. Our method aimed to improve accuracy and intuitive ness of classification model using fuzzy concepts and hierarchical relations. We also proposed a generalization-based criterion for ranking associative classification rules to improve classifier accuracy. The model accuracy tested on UCI standard database.

Enhanced F-Perceptory Approach for Dealing With Geographic Data Imprecision From the Conceptual Modeling to the Fuzzy Geographical Database Building

When analyzing spatial issues, it is often that the geographer is confronted with many problems concerning the uncertainty of the available information. These problems may appear on the geometric or semantic quality of objects and as a result, a low precision is considered. So, it is necessary to develop representation and modeling methods that are suited to the imprecise nature of geographic data. This leads proposing recently F-Perceptory to manage fuzzy geographic data modeling. From the model described in Zoghlami, et al, (2011) some limits are relieved. F-Perceptory does not manage fuzzy composite geographic objects. The paper shows proposition to enhance the approach by the managing this type of objects in modeling and its transformation to the UML. On the technical level, the object modeling tools commonly used do not take into account fuzzy data. The authors propose new functional modules integrated under an existing CASE tool.

Enhanced F-Perceptory Approach for Dealing with Geographic Data Imprecision from the Conceptual Modeling to the Fuzzy Geographical Database Building

When analyzing spatial issues, it is often that the geographer is confronted with many problems concerning the uncertainty of the available information. These problems may appear on the geometric or semantic quality of objects and as a result, a low precision is considered. So, it is necessary to develop representation and modeling methods that are suited to the imprecise nature of geographic data. This leads proposing recently F-Perceptory to manage fuzzy geographic data modeling. From the model described in Zoghlami, et al, (2011) some limits are relieved. F-Perceptory does not manage fuzzy composite geographic objects. The paper shows proposition to enhance the approach by the managing this type of objects in modeling and its transformation to the UML. On the technical level, the object modeling tools commonly used do not take into account fuzzy data. The authors propose new functional modules integrated under an existing CASE tool.