Since the resulting regulators are derived from approximations of the nonlinear dynamics, they are further enhanced in real-time with online learning/adaptive optimization, according to performance measurements.
In this paper, a computable model derived from step approximation method was present to theoretically determine the coupling loss.
By considering two model problems, our numerical experiments indicate that one-way wave equations derived from Pade approximation perform best as numerical absorbing boundary conditions for a class of problems of interest in the simulation of electromagnetic wave propagation.
From now on, we will refer loosely to the elemental distance Markov chain, meaning, when appropriate, the Markov chain derived from this approximation.
The correction term is derived from an approximation of the difference between the exact solution of stochastic differential equations and the Milstein continuous-time extension.
This value was derived from the approximation L e (r) = exp(− βr) with β = −0.173 which covers 97.6percentt of the population [ 32].
Tables 1 and 2 also provide 95% confidence intervals derived from Gaussian approximations of the distribution of the sum of strata frequencies or sum of ratios of strata frequencies.
Note that if we set f(N) = 1, we get the AIC score as (5) log P (D | S ) = log P (D | θ ^ S, S ) - C (S ) Alternatively, if we set f(N) = 1/2 log(N), then we obtain the BIC score as (6) log P (D | S ) = log P (D | θ ^ S, S ) - 1 / 2 C (S ) log (N ) The BIC score and AIC score are derived from some approximations when the number of samples N approaches infinity [ 25].
This can be derived from the normal approximation, given that the estimate of type-1 error rate is distributed as an observed probability of success for a binomial random variable with a success probability of 0.05 under no inflation of type-1 error and the sample size of 1000, which is the number of data replicates.
This can be derived from the normal approximation, given that the estimate of type-1 error rate is distributed as an observed probability of success for a binomial random variable with a success probability of 0.05 under no inflation of type-1 error and the sample size of 200, which is the number of phenotype replicates.
One approximation that is commonly used for the logistic score (Wu et al., 2011) is derived from the Laplace approximation to the quasi likelihood (Breslow and Clayton, 1993).
