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This paper presents an approach for robust open-loop and closed-loop control of biological production processes described by models with parameter uncertainties.
In particular, the rendezvous process is divided into in-plane motion and out-plane motion based on C W equations, and the relative motion models with parameter uncertainties are established.
Models with parameter estimates, parameter bias and confident intervals [52] are shown in the table 2. Vole results showed that the best approximating model for the data was a logistic model (model 27v) including both trophic terms arising from the density ratios of predator to prey (kestrels/voles) and prey to food resource (voles/rain) and a positive effect of mean annual temperature (Table 3).
The models were formulated as hierarchical Bayesian models with parameter estimation via Markov chain Monte Carlo simulation (MCMC).
The complexity of the problem is increased when additional constraints on the parameters (e.g. the palindrome constraint in the popular MEME algorithm) are employed, as the maximum likelihood value of models with parameter constraints will be lower than unconstrained models of the same motif width.
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The simulation models with parameters identified from finite element analysis and the experiments are developed.
Consider the case where such models with parameters {θ = (θ i ) ∈ Θ} are given by likelihood L(θ ǀ data).
We discuss recent progresses in computational studies of membrane proteins based on physical models with parameters derived from bioinformatics analysis.
They can be applied to models with parameters estimated using maximum likelihood methods.
In this study, we modeled skin mechanics using hyper- and visco-elastic material models with parameters fitted to values from mammalian tissues.
Moreover, the properties of their voltage-sensitive membranes were approximated with Hodgkin-Huxley type membrane models, with parameters adjusted to simulate in vivo recordings to current injections.
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