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Let P be a generic probability mass function or density function, with its meaning given by the context.
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In contrast, the preferential distribution vector h in Equation (5) is a generic probability distribution, which is flexible thereby more powerful.
We assume conditional independence in the sense that p (y | β, τ ) = ∏ i = 1 N p (y i | β i, τ i ), where p is a generic notation for a probability density function or a probability mass function, τ = (τ 1, …, τ N ), β = (β 1, …, β N ) and β i = (β i 0, β i 1, β i 2 ) for i = 1, …, N. The conditioning on t is ignored in the notation for ease of exposition.
Here is a generic formulation of the problem: Let p(x) be some probabilities (possibly marginal) defined for x = 1,..., N. What are the peaks in p(x)?
This is a generic problem.
Or it's a generic restaurant.
Hence this is a generic process.
It's a generic field now.
EQ-5D is a generic HRQoL instrument.
Mobyle is a generic web-based framework.
RNA abstract shapes are a generic concept.
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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