Sentence examples for model parameter distributions from inspiring English sources

In this research, the sensitivity of microbial growth model parameter distributions with respect to data quality and quantity is investigated using Monte Carlo analysis.

Posterior model parameter distributions were estimated using the Markov Chain Monte Carlo sampling code DREAMZS and a log-likelihood function assuming heteroscedastic, t-distributed residuals.

A Bayesian calibration procedure is employed and the posterior model parameter distributions obtained at one unit-problem level are used as prior distributions for the same parameters in the next-tier simulations.

Monte Carlo Markov Chain (MCMC) methods were used to estimate model parameter distributions.

A Bayesian approach is applied to infer model parameter distributions.

It then proceeded iteratively to maximize the likelihood of the entire model parameter distributions given the observed serum amikacin concentrations.

There appears to be a linear relation between data quality (expressed by means of the standard deviation of the normal distribution assumed on experimental data) and model parameter uncertainty (expressed by means of the standard deviation of the model parameter distribution).

When using a priori information of a PBPK model structure combined with Bayesian information about PBPK model parameter distribution, the administered activity could be determined with acceptable accuracy using only two time points (4 h, 2 d) and thus allow a considerable reduction of needed data for individual dosimetry.

SBMCMD relies on sampling from the model parameters distribution using Markov Chain Monte Carlo, MCMC, methods.

This implies that the minimum average code length is obtained only for the correct source distribution (model parameters); in other words, the choice of wrong model parameters (distribution function) leads to larger code lengths.

Risk assessment model input parameter distributions.