The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function – the transfer function – and a framework for the identifiability analysis of complex model structures using linked models.
This article does not consider the well-testing analysis of more complex models using the presented approach that could be the subjects of future studies.
It can be useful also in the analysis of more complex models in which demands for inputs depend on strategic interaction between producers of inputs and their customers (as in [5]).
This may be necessary either to simulate large models with heterogeneous neural types, or to simplify simulation and analysis of detailed, complex models in a large simulation by isolating the new model to a small subpopulation of a larger overall network.
Compared with the previous method that systematically analyzed two-dimensional parameter space in the per-time feedback model [10], MAR enables more precise analysis of more complex models, the interlocked feedback model that contains the per-tim and dclk-cyc loops.
The growing need for uncertainty analysis of complex computational models has led to an expanding use of meta-models across engineering and sciences.
Here we present a new, generic approach for explorative analysis of complex patterning models which focuses on the essential pattern features and their relations to the model parameters.
In addition from this sample, we can compute and observe correlations between parameters, produce desired plot of the marginal posterior distributions, etc. Flexible software for Bayesian analysis of complex statistical models by using MCMC methods.
In the case of the ZIP regression model with Co-varying [30], the Gibbs sampler can proceed with the following ways: The Bayesian inference using Gibbs sampling (BUGS) project has developed a flexible software for Bayesian analysis of complex statistical models by using MCMC methods [30].
