Comments on "Statistical reasoning with set-valued information: Ontic vs. epistemic views"
نویسنده
چکیده
In information processing tasks, sets may have a conjunctive or a disjunctive reading. In the conjunctive reading, a set represents an object of interest and its elements are subparts of the object, forming a composite description. In the disjunctive reading, a set contains mutually exclusive elements and refers to the representation of incomplete knowledge. It does not model an actual object or quantity, but partial information about an underlying object or a precise quantity. This distinction between what we call ontic vs. epistemic sets remains valid for fuzzy sets, whose membership functions, in the disjunctive reading are possibility distributions, over deterministic or random values. This paper examines the impact of this distinction in statistics. We show its importance because there is a risk of misusing basic notions and tools, such as conditioning, distance between sets, variance, regression, etc. when data are set-valued. We discuss several examples where the ontic and epistemic points of view yield different approaches to these concepts.
منابع مشابه
Comparing uncertainty data in epistemic and ontic sense used to decision making problem
In the paper aspect of comparability alternatives in decision making problem by imprecise or incomplete information isexamined. In particular, new definitions of transitivity based on the measure of the intensity preference between pairsof alternatives in epistemic and ontic case is presented and its application to solve decision making problem is proposed.
متن کاملComments on "A distance-based statistical analysis of fuzzy number-valued data" by the SMIRE research group
This paper is a fine review of various aspects related to the statistical handling of “ontic” random fuzzy sets by the means of appropriate distances. It is quite comprehensive and helpful, as it clarifies the status of fuzzy sets in such methods, explains the advantages of using a distance-based approach, specifies the pitfalls in which one should not fall when dealing with “ontic” random fuzz...
متن کاملLearning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization
Methods for analyzing or learning from “fuzzy data” have attracted increasing attention in recent years. In many cases, however, existing methods (for precise, non-fuzzy data) are extended to the fuzzy case in an ad-hoc manner, and without carefully considering the interpretation of a fuzzy set when being used for modeling data. Distinguishing between an ontic and an epistemic interpretation of...
متن کاملAnswer Set Programming and Planning with Knowledge and World-Altering Actions in Multiple Agent Domains
This paper discusses the planning problem in multi-agent domains, in which agents may execute not only world-altering actions, but also epistemic actions. The paper reviews the concepts of Kripke structures and update models, as proposed in the literature to model epistemic and ontic actions; it then discusses the use of Answer Set Programming (ASP) in representing and reasoning about the effec...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Int. J. Approx. Reasoning
دوره 55 شماره
صفحات -
تاریخ انتشار 2014