Premium
On a Role for Copula's in Jeffrey's Rule with An Application to Decision Making
Author(s) -
Yager Ronald R.,
Alajlan Naif
Publication year - 2015
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21736
Subject(s) - copula (linguistics) , conditional probability , computer science , joint probability distribution , conditioning , econometrics , event (particle physics) , artificial intelligence , operations research , data mining , statistics , mathematics , physics , quantum mechanics
We introduce Jeffrey's rule of conditioning. We explain how it enables us to determine the current probability of an event using a collection of conditional probabilities of the event determined from past experiences and the current probabilities of the conditioning events. We note the importance of the joint probabilities of the event of interest and the conditioning events in obtaining the required conditional probabilities. We investigate the use of copula's to help obtain these required joint probabilities. We then apply our results to a problem of financial decision making in which the success of the stock issue of a new company depends on the quality of management of company. Here, past history tells information about the success of a typical company based on its quality of management and our own observations provides information about the quality of the current companies management.