Sentence examples for conditional approximation from inspiring English sources

The model was fit using the first-order conditional approximation to the likelihood in NONMEM.

In this paper, we consider approximation of the population likelihood by using the first-order conditional estimation with interaction (FOCEI) approximation of the population likelihood together with the EKF for state estimation on the individual level.

Data sets were analyzed with NONMEM 7.2 using first-order conditional estimation with interaction and stochastic approximation expectation maximization algorithms.

The most commonly used approximation is the first-order conditional estimation method where L is linearised with a first order Taylor expansion around the estimates of the random effects, i.e. around η.

Approaches to the parameter estimation problem on a population level include for example the first-order (FO) and the first-order conditional estimation (FOCE) method (20, 21) and stochastic approximation of expectation maximization (SAEM) method (22).

Plan et al. investigated parametric approaches for maximum likelihood estimation: first-order conditional estimation in NONMEM and R, LAPLACE in NONMEM and SAS, adaptive Gaussian quadrature in SAS, and stochastic approximation expectation maximization in NONMEM and MONOLIX (both stochastic approximation expectation maximization approaches with default and modified settings).

The stochastic approximation expectation maximization method with importance sampling as implemented in NONMEM was used to obtain the estimates of standard errors for the biological models in our analysis, because of numerical difficulties with the first-order conditional estimation method.

a random intercepts regression model after integrating over the estimated distribution of random effects ('RE-integ'), a random intercepts regression model after rescaling the estimated conditional coefficients using the approximation of Zeger ('RE-approx'), a marginal regression model fitted by GEE with exchangeable correlation structure ('GEE'GEE

In a simulation study the model distribution of the conditional bias (conditioned on a simulated reality) of the variance approximations is estimated for various variograms and two sample sizes.

Rules are interpreted as order-of-magnitude approximations of conditional probabilities which impose constraints over the rankings of worlds.

This approach is based on a computationally efficient approximation to the conditional out-crossing rate for higher-dimensional vectors.