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We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels.
We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches.
Reichenbach was the first to formalize this idea rather precisely.
To formalize this idea, we rely on a classical worst-case approach to uncertainty in the spirit of, e.g., Gilboa and Schmeidler (1989).
We will formalize this idea in the following, where the index λ refers to local maxima of test case quality (tilde {E}).
To formalize this idea, suppose that (delta _{c}=0) and (delta _{l}>0) are the liquidation costs for the central and the local supervisor, respectively, in the absence of supervisory responsibilities, and that such responsibilities add (Delta ) to these costs.
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There are a number of important motivations for formalizing this idea of a model.
Corollary 2 formalizes this idea.
Subsequently, Shannon (1948) formalized this idea by defining the entropy and mutual information concepts.
One common way of formalizing this idea is by means of a scale from 0 to 1, where 0 indicates absolute certainty in the falsity of a proposition, 1 indicates absolute certainty in its truth, and.5 indicates that the subject regards the proposition just as likely to be true as false.
Granger formalized this idea in the context of linear regression models of stochastic processes [4].
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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