Nested sampling has been used for DNA motif selection [ 24], and an application to model selection in systems biology has recently been reported [ 25] where the MultiNest package developed for cosmology [ 10] was used to compare signal transduction models of 4, 5 and 6 parameters.
Results: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework.
For statistical inference, we use approximate Bayesian computation (ABC), a statistical approach that has successfully been applied to parametric inference and model selection in dynamical systems (Toni et al., 2009) to infer posterior probability distributions both for individual models and the parameters of the models given experimental data.
Illustration of the dual-hop FGAF relay selection system model.
With the utilisation of fuzzy decisions for the configuration system model, selection of optimal modular tool configurations is done.
This includes hardware design, sensor and actuator selection, system modeling, parameter identification, controller design and experimental testing.
An efficient algorithm recently developed for ABC using the sequential Monte Carlo method was implemented for parameter estimate and model selection for nonlinear dynamic systems [ 24, 25].
Kajioka et al.[31] applied an attractor selection model to a multi-interface selection system using several wireless media such as LTE, WiMAX, and WiFi with different communication capabilities.
The expert system technology is successfully integrated into the system to provide help for model parameter selection or model selection, and to make the numerical model system more accessible for non-expert users.
Figure 8 ToxPredict Step 3 - Model Selection, Interaction of OpenTox Services: User-System Interaction.
