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It has been long known that Gibbs sampler is useful for simulating data from the joint posterior distribution (Gelfand et al.1990 Wakefield19966).1990 Wakefield1996
ABC approaches bypass the computational difficulties of using explicit likelihood functions by simulating data from a coalescent model, and thus provide high degree of freedom in the choice of demographic models to be tested.
This was accomplished by repeatedly simulating data from a model where the true among-sire variance was zero.
Significance cutoffs were obtained by repeatedly simulating data from a neutral coalescence model using a value of θ estimated from the data by taking the average number of segregating sites in a window.
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Before evaluating simulated data from the network model, simulations generated from the distribution itself were tested.
The data we use are simulated data from a controlled random walk.
Results from simulated data from the PAMELA prototype calorimeter will be shown.
These were combined to produce simulated data from different experimental designs and treatment effects.
The parameters are estimated and compared for simulated data from these designs.
In this paper, a PMSA methodology based on simulated data from regional climate models is developed.
The networks were developed based on the validated simulated data from MCNPX code.
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