Sentence examples for fitting iteration from inspiring English sources

The model parameters are identified through the least-square fitting iteration method, of which the undetermined model parameters are updated in iteration to fit the dynamic reflection coefficient curve with the wave-based one.

For the dots-reversal task we fit the same model, only here treating noise as a latent variable like we did for fitting the normative model and deriving the time-evolution of the log-odds probability distribution over each trial and each step of the fitting iteration: L i s m n = (1 − K i s ) L i s m − 1, n + k i C i s m n + 2 k i 〈 | C i s n | 〉 η i s m n.

Because we could not directly observe this noisy quantity, we fit the model by numerically deriving the time-evolution of the log-odds probability distribution over each trial and each step of the fitting iteration.

Due to the dependency of the results from each fitting iteration on the previous iteration, there may be other directions in the parameter space that could also give possible solutions.

Genes were assessed for differential expression using Cyber-T [ 38] version 8.01 with the following settings: the Bayesian prior estimate was 10, the sliding window size was 101, and the β-fit iteration value was 2. We defined the cutoff value for differential gene expression as transcripts that showed a >2.0-fold change and a Bayesian t-test P value of < 0.001.

The common effect variance σ a 2 was sampled from a scaled inverse Chi-Square with degrees of freedom ν ~ = ν + ν M (t ) and scale S a 2 ~ = ν S a 2 + ∑ k = 1 K a k 2 ν ~, where ν M (t ) is the number of SNPs fitted in iteration t.

It allows dealing with overlapping polyads and includes more efficient and faster algorithms for the calculation of coefficients related to molecular symmetry properties (6C, 9C and 12C symbols for C3v, Td, and Oh point groups) and for better convergence of least-square-fit iterations as well.

The algorithm sequentially builds a set of predictive models by fitting at each iteration the residuals of the previous predictive model.

Because of the nature of the MCMC sampler used in the model fitting, at each iteration the observed values and the current imputed values of and were used to estimate φ a and φ r respectively; in turn φ a and φ r then impute another set of missing values of and.

Version 1.0 of M3O includes Deterministic and Stochastic Dynamic Programming, Implicit Stochastic Optimization, Sampling Stochastic Dynamic Programming, fitted Q-iteration, Evolutionary Multi-Objective Direct Policy Search, and Model Predictive Control.

Two different data-driven approaches to prediction and optimization are used to demonstrate the methodological flexibility: (i) a combination of Bayesian regularized neural networks with genetic algorithm based optimization, and (ii) a reinforcement learning based control logic using fitted Q-iteration are both successfully applied.