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We further define the belief probability that node is in the neighborhood of in the future as expressed by (1).
Also, for Model II, the belief probability in the next time slot will equal to zero since the primary user will be active with probability one.
Let b t) and (hat {b}(t)) denote the belief probability and the prediction probability, respectively, of the channel being in idle state at time slot t.
This is encouraging as the smaller KL-divergence values indicate that the distribution of the belief probability in the future is close to the distribution of that in the past.
Let π(t)= [ π 0 t) π 1 t)] be the belief probability vector of a channel at time t, where π i (t) is the probability of the hidden process staying in state s i.
The belief state L t) in the t th time slot will be updated based on the feedback signals F(t−1), and F(t−2) at the end of the t−1th and t−2th time slots, respectively, the last belief probability L(t−1) = u Δ, and the sensing outcome γ(t−1).
Similar(52)
The information of verified faults and observed symptoms from the current fault localization process is used to update the belief probabilities to increase the accuracy of the model.
Later we will see that there are good reasons to distinguish inductive probabilities from Bayesian degree-of-belief probabilities and from purely logical probabilities.
I want to see belief defeat probability.
In this context, belief means probability 1.
They consider the case where each node transmits its belief (conditional probability) to other nodes.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com