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Thus it is a log-linear model (a distribution whose logarithm is linear in the features)—a type of model now prevalent in many statistical NLP tasks.
This paper proposes an architecture of "road network – distribution network – large scale EVs" for further research, upon which a road network model, a distribution network model (charging stations included) and a large scale EVs model are established.
The essence of the method is to initially assign to each parameter of a model a distribution function reflecting the current uncertainty of its value and to refine these estimates from new information, provided by the data.
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Wagner, Pohlad and the rest have gained a beachhead in Hollywood because the movie business has grown and the studio business has shifted somewhat from a production model to a distribution model.
GP regression is a Bayesian non-parametric which models a distribution over an infinite set of random variables.
Then, a simulation model for a distribution network with two inverter-based DGs is developed.
Within each population, the model predicts a distribution for the number of infected cats (male or female).
This model enforces a distribution of the fitted rates greatly different to the data (Fig. 3A, α = 0.33 s−1).
Our fully Bayesian model produces a distribution over probable clusterings.
Fitting a WLC model yields a distribution of both contour and persistence length.
Finally, the partition function Z(θ(t )) in a MRF normalizes the model to a distribution.
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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