Belief Evolution Network-based Probability Transformation and Fusion
نویسندگان
چکیده
Smets proposes the Pignistic Probability Transformation (PPT) as decision layer in Transferable Belief Model (TBM), which argues when there is no more information, we have to make a using Mass Function (PMF). In this paper, Evolution Network (BEN) and full causality function are proposed by introducing Hierarchical Hypothesis Space (HHS). Based on BEN, interpret PPT from an information fusion view propose new (PT) method called Full Causality (FCPT), has better performance under Bi-Criteria evaluation. Besides, heuristically probability based FCPT. Compared with Dempster Rule of Combination (DRC), reasonable result fusing same evidence. • A model Basic Assignment. BEN-based proposed. PT (PCR) The PCR than existing methods.
منابع مشابه
Uncertainty, Belief, and Probability
We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasurable event (one to which we do not assign a probability), we can talk about only the inner measure and outer measure of the event. Thus, the measure of belief in an event can be represented by an inter...
متن کاملProbability, rational belief and belief change
A simple model of rational belief holds that: (i) an instantaneous snapshot of an ideally rational belief system corresponds to a probability distribution; and (ii) rational belief change occurs by Bayesian conditionalization. But a priori probability distributions of the Kolmogorov sort cannot distinguish between propositions that are simply true from propositions that are necessarily true. Fu...
متن کاملConditional Belief, Knowledge and Probability
A natural way to represent beliefs and the process of updating beliefs is presented by Bayesian probability theory, where belief of an agent a in P can be interpreted as a considering that P is more probable than not P. This paper attempts to get at the core logical notion underlying this. The paper presents a sound and complete neighbourhood logic for conditional belief and knowledge, and trac...
متن کاملVarieties of Belief and Probability
For reasoning about uncertain situations, we have probability theory, and we have logics of knowledge and belief. How does elementary probability theory relate to epistemic logic and the logic of belief? The paper focuses on the notion of betting belief, and interprets a language for knowledge and belief in two kinds of models: epistemic neighbourhood models and epistemic probability models. It...
متن کاملUpdate, Probability, Knowledge and Belief
The paper compares two kinds of models for logics of knowledge and belief, neighbourhood models and epistemic weight models. We give sound and complete calculi for both, and we show that our calculus for neighbourhood models is sound but not complete for epistemic weight models. Epistemic weight models combine knowledge and probability by using epistemic accessibility relations and weights to d...
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: Computers & Industrial Engineering
سال: 2022
ISSN: ['0360-8352', '1879-0550']
DOI: https://doi.org/10.1016/j.cie.2022.108750