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The cubic spline model requires 70+ clusters parameters to be sampled per cycle.
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Although this provides more accurate models for the topological constraints, it also introduces an additional complexity during the decoding process as the normalized development time becomes an additional parameter to be sampled.
In csGM-LDA, the parameters {α,β,μ 1 C,σ 1:C } are dataset-level parameters, assumed to be sampled once in the process of generating a set of images.
This works reasonably well for a low dimensional parameter space and if the target density function, i.e. the parameter space to be sampled, is well-behaved [ 14].
In the case of non-identifiability, the parameter space to be sampled is not restricted rendering convergence near to impossible.
If such an interaction were to be described to the classic matching algorithm, the unimportant dihedral parameters would have to be sampled over the whole 360 degree range, resulting in long running times.
The major challenge is the complex nonlinear interrelation between parameters and model responses which requires that the parameter space has to be sampled densely to capture all scenarios of model predictions.
Although the method would require extensive computation when a large number of parameter sets is considered, our analysis shows that only a small fraction of the parameter space needs to be sampled to produce reliable results.
Hence, the parameter space needs to be sampled densely in the initial experimental design to ensure that all possible solutions are found.
In order to use Gibbs sampling, the probability of each model parameter must be able to be sampled given all the other parameters.
This reduces the number of parameters to be estimated and thus sampling errors.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com