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Specifically, the existence of stochastic actuator faults are described by using the Bernoulli distribution.
On assumption that the input variables x n are independent of each other, the likelihood on the entire set of training samples can be calculated using the Bernoulli distribution: P ( t w ) = ∏ n = 1 N σ y x n ; w t n 1 - σ y x n ; w 1 - t n (12).
Given a coefficient vector B4 ∈ ℜ P 4×1 (all zeros but the first ten), the probability of disease π was computed by using (20) π = exp X 4 B 4 1 + exp X 4 B 4. Then, the binary class label block was generated using the Bernoulli distribution with the probability π.
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All of these were identified using the Bernoulli model.
Results were consistent with SaTScan cluster detection using the Bernoulli probability model.
Using the Bernoulli model, clusters with increased relative risk were identified.
Pulmonary artery systolic pressure was calculated according to velocity of tricuspid regurgitation using the Bernoulli equation.
Due to the high prevalences, some of the adjusted GLM analyses could not converge using the Bernoulli family.
To obtain a pre-trained RBM, we trained all the hidden layers by using the Bernoulli-Bernoulli RBM.
A series of variables of the randomly occurring phenomena obeying the Bernoulli distribution is used to govern ROIDs and RONs.
In (25), not λ but ( frac{N+2lambda }{4} ) is used because not {0, 1} of the Bernoulli distribution but {−1/2, 3/2} is the value domain for I n + k |Q c,k | k = 0, 1, …, N − 1).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com