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Methods: the model was developed using the WinBUGS software.
Methods: The model reproduced the historical data on primary and revision knee replacement implants obtained from the literature and the Nationwide Inpatient Sample.
METHODS: The model is comprised of cortical pyramidal, cortical inhibitory, thalamocortical relay, and thalamic reticular single-compartment neurons, implemented with Hodgkin-Huxley model ion channels and connected by AMPA, GABAA, and GABAB synapses.
Using Latin Hypercube Sampling and other sampling methods, the model is evaluated to find these performance characteristics when variability in the model׳s geometric and physical parameters is specified.
Based on Monte Carlo methods, the model has the capability to perform multiple simulations by changing random variables of tunnel users such as pre-movement times and walking speeds.
By applying the ordinary least-squares estimation (OLSE), bootstrapping (BOOT), and leave-one-out cross-validation (CROS) methods, the model parameters are estimated and analysed with respect to outlier detection.
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In this method, the model parameters are found by ensuring that simulated data from the model and available empirical data produce similar auxiliary statistics.
For the physics-based method, the model parameters are kept constant and will not be updated.
By running a series of simulations (known commonly as the Monte Carlo method), the model allows us to quantify the uncertainty associated with the current polling snapshot.
Perhaps most important, by running a series of simulations (known commonly as the Monte Carlo method), the model allows us to quantify the uncertainty associated with the current polling snapshot.
Also, because of the flexibility of the PLSR method, the model will mimic the characteristics of the surveillance system, perhaps hiding characteristics particular to web queries.
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