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It is now possible to fit the values instead of approximating them as it is the case with linear functions.
Instead of approximating the analytically intractable parameter posterior, one can choose a proposal density and apply IS to jointly adjust the state and parameter samples.
In the first step, instead of approximating the complete model, we approximate a reduced model with a smaller number of unknowns.
To reduce the complexity of measurement, instead of approximating the information of the distribution for the specified random variable, we can consider an outage event as follows.
Recent research [10, 11, 12, 13, 14] has developed calibration methods that simultaneously estimate all model inputs and parameters, while using the outputs of the model directly (instead of approximating the linkage between the calibration variables and the data).
Instead of approximating the independent Q-factor, the approximate rollout action (widetilde {a}^{RL}(m)) is obtained by maximizing the approximation of the Q-factor difference (Q_{m}(a -Q_{m}(a -Q_{mcal {H}})), widetilde{a^{mathcal = argmaxlimits_{a in {matH}}ak{{A}}}}left{widetilde{a}_{m}(a)-widetilde{Q}_{m}(a^{RL}hcal {H}})right} (54).
Similar(54)
The particle filter, instead, seems capable of approximating the optimal filter in the same situation.
Instead of approximate modeling, several frequencies at the crossover band are marked and their average phase margins from the distribution plot are picked to represent the real uncertain system.
The results clearly depict that the neural network is a powerful tool to estimate the reaction rate and the designed neural network can be used instead of approximate and complex analytical equations.
Sampling from (12) is computationally more intensive, especially when N is large, but our method is based on a direct approximation of p(a 1 i |a i+1:n,y 1 n ) based on (left (a^{k}_{1:i-1}right)_{1le k le N}) and a i+1:n instead of approximate MCMC draws.
That is, we try to better understand the border between tractable and intractable cases of MTO while sticking to optimal (instead of approximate) solutions.
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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