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The mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make (hard) decision, but for contrariwise to erroneous widespread idea/claim, this is not the only interest for using such mappings nowadays. Actually the probabilistic transformations of belief mass assignments are very useful in modern multitarget multisensor tracking systems where one deals with soft decisions, especially when precise belief structures are not always available due to the existence of uncertainty in human being’s subjective judgments. Therefore, a new probabilistic transformation of interval-valued belief structure is put forward in the generalized power space, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the new transformation works and we compare it to the main existing transformations proposed in the literature so far. Results are provided to illustrate the rationality and efficiency of this new proposed method making the decision problem simpler.
The mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make (hard) decision, but for contrariwise to erroneous widespread idea / claim, this is not the only interest for using such mappings nowadays. Actually the probabilistic transformations of belief mass assignments are very useful in modern multitarget multisensor tracking systems where one deals with soft decisions, especially when precise belief structures are not always available due to the existence of uncertainty in human being’s subjective judgments. Therefore, a new probabilistic transformation of interval -valued belief structure is put forward in the generalized power space, in order to build a subjective probability measure from any basic belief assignment that any of the models of the frame of discernment. Several examples are given to show how the new transformation works and we compare it to the main existing transformations proposed in the literature so far. Results a re provided to illustrate the rationality and efficiency of this new proposed method making the decision problem simpler.