Sentence examples for model parameters prior from inspiring English sources

For this exercise we assumed that approximately 1 in 4 women with minor toxicity would discontinue treatment.[42] We compared the differences between estimates of life expectancy derived from the model calibrated to the 24-month short-term data (using the mean of the 50 best-fitting sets) to those generated using the original model parameters prior to calibration.

As an option, it performs maximum likelihood estimation of model parameters prior to coevolution analysis, and supports a wide range of commonly used model of evolution, from Jukes-Cantor to General Time Reversible for nucleotides, and the Dayhoff and JTT models for proteins, using the dcmutt implementation [ 23].

The tree prior and mutation model parameter priors used for the real data analyses are the same as the ones for analyses of the simulation data.

The analyses with Beast differed from those with MrBayes in that, with Beast, a molecular clock was applied to the tree and specified calibration point (date) priors were used in addition to substitution model parameter priors.

This seems to be true only for monophonic audio signals, and even in this case, estimating the model parameters without prior knowledge on the fundamental frequency range is not a trivial task.

In processes involving complex flow pattern such as two-phase flows, it is difficult to represent the model parameters in a prior form.

The model computes the posterior distribution over all parameters and Table  1 lists all model parameters and the prior distributions adopted.

23 24 In this application of approximate bayesian computation, we first simulated large numbers of female population datasets using model parameters drawn from prior distributions.

It is important to note that the posterior distributions listed in Table 2 are conditional upon fixed model parameters (Table 1), prior distributions of calibrated parameters (Table 2), and the data sets used in calibration.

All model parameters, including class priors and attribute probability distributions, can be estimated from the training set with the method of maximum likelihood.

Therefore, a limited series of static experiments had to be conducted to obtain usable prior model parameters estimates.