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These results held true even when the Bayesian fractional additivity model employed a biased prior.
Our Bayesian inference model employed a relatively simple generative model sufficient to explain the time-varying character of adaptation we focused on.
The model employed a fully Bayesian approach using Markov Chain Monte Carlo (MCMC) techniques for inference and model-checking.
The model employed a Monte Carlo simulation, whereby a single patient was followed through the Markov process in monthly cycles over a period of one year.
The model employed a method based on wavelet packet decomposition transforming protein sequences into energy feature vectors for training an artificial neural network (ANN) model.
The model employed a fully Bayesian approach using Markov Chain Monte Carlo (MCMC) techniques for inference and model checking (17 18).
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The first model employs a derivation of the Carnot efficiency.
In addition, a parametric failure prediction model employing a modified characteristic curve method was established.
The simulation model employs a numerical solution to a set of differential equations describing the system.
The model employs a well-known two layer representation of the boundary layer structure.
The first one, like the TVAR model, employs a basis expansion of the filter coefficients.
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