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Let x (t) and a (t) denote the state and action at decision epoch t, respectively, and consider an infinite-horizon MDP modeling where the discrete decision making process is assumed to be infinitely long.
Regular and semi-regular methods of solution are to be recommended in microflows modeling where the small departures from equilibrium condition and flow unsteadiness impair the efficiency of traditional direct simulation Monte Carlo particle schemes.
This is particularly problematic in uncertainty modeling where the computational effort is usually increased significantly by the need for multiple runs (e.g. when conducting a Monte Carlo analysis) to achieve reliable results.
It is worth remembering here the classical work in mathematical modeling where the data of previous history were used.
This is particularly true in cognitive modeling, where the model consists of unformalized concepts and their connections.
We present an individual plant modeling, where the main constraints related to a hydrothermal system are considered.
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So we've developed a tool which attempts to model where the UK's carbon emissions come from.
However, the data analysis strategy and the postulation of a physically valid model for this kind of measurements may be tackled with many different approaches ranging from pure soft-modeling (model-free) to hard-modeling, where the elaboration of a parametric spectro-temporal model may be required.
"We are for a model where the majority decides for itself".
Most recently this has been achieved by beefing up the apprenticeship model, where the training element is "employer-led".
It's a much older one: the drug-dealer business-model, where the first taste is free.
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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