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What the brain needed to do was figure out how probable it was that this particular prior was correct, and how probable it was that the new sensory information was correct, and crunch those two probabilities together to come up with an answer.
In our case, we considered two probabilities.
Note that there are two probabilities associated with fuzzy modeling.
(PR1) fails because the two probabilities are equal; (PR2) fails because the second term is undefined.
In building its representativeness argument, MBPTA considers two probabilities, as shown in Fig. 1d.
These two probabilities bound the probability of false alarm of the whole system.
To calculate this probability, we need to calculate the following two probabilities.
Note that the two probabilities PFA and PD are correlated with the predefined threshold parameters.
Denote these two probabilities by η R and η P =1−η R, respectively.
Then, the channel state can be expressed as a function of two probabilities.
Consider aggregating these two probabilities by taking an unweighted average of them: $r = p/2 + q/2$.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com