Exact(8)
These soft-decision decoding algorithms iteratively update the likelihood probability estimation of each bit.
Finally, while building the ensemble, the architecture of each individual classifier is decided by sampling this likelihood probability distribution.
This coefficient quantifies the likelihood probability for a mobile node to get transited into a selfish node.
The observation variable is distributed according to p y t |x t ), defined as the observation likelihood probability.
The likelihood probability modeled above in Eq. (14) is defined only for a discrete set consisting of the source training vector.
Risk refers to the likelihood (probability of event occurring) plus the likely impact of the threat (without mitigation and contingency measures).
where, as stated above, the likelihood probability (pleft (mathbf {x}_{t}|{mathbf {y}_{t}^{k}}right)) is derived from the measurement model (Eq. (2)).
Additionally, as in TC-INCA [13], we use context vectors instead of single vectors in order to improve the estimation of the likelihood probability.
Related(18)
risk probability
likelihood susceptibility
likelihood forecast
rate probability
potential probability
likelihood possibility
likelihood theory
likelihood probabilistic
likelihood expectation
likelihood rate
likelihood hazard
possibility probability
chance probability
likelihood random
likelihood chance
opportunity probability
likely probability
probabilities probability
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