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Section 3 reverts them to z-scores again and transforms the numbers into normal percentages, i.e. we calculate the area under the normal density to the left of the z-score.
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The normal distribution is produced by the normal density function, p(x) = e−(x − μ 2/2σ2/σ√2π.
For a=b=c=1, it reduces to the normal density.
The scaled-t and double exponential (DE) densities have higher mass at zero and thicker tails than the normal density.
Then PROB-STD is the area under the normal distribution probability density function (PDF) centred at (hat{y}left( {{mathbf{x}}_{new} } right)) with the standard deviation (hat{sigma }) from −∞ to 0.5 (decision value) if class 1 is predicted (i.e., (hat{y}left( {{mathbf{x}}_{new} } right) > 0.5)) and from 0.5 to +∞ if class 2 was predicted.
where f is the bivariate normal density function.
Where φ and Φ are, respectively, the standard normal density function and standard normal distribution functions.
The set of Gaussian functions is closed under multiplication, i.e. the product of normal densities is also a normal density, with the mean and the variance of the product density given by μ = ∑ μ i σ i 2 ∑ 1 σ i 2 and σ 2 = 1 ∑ 1 σ i 2 respectively.
In Bayes B the slab is a scaled- t density, while in BayesC the slab is a normal density.
The use of this type of promoter is advantageous, as the engineered strains can be driven under the normal growth, on different carbon sources, or under a high cell density immobilization condition.
+++ Normal density, ++ moderately decreased density, + severely decreased.
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