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Importantly, we control the sparsity of reconstruction weights by using prior density based on the Laplacian scale mixture (LSM) distribution [25].
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This notion can be captured by using prior plausibilities, an analogue to prior probabilities, which can be updated by "conditioning".
In [10], Bayesian learning was performed for sparse representation of image data where Laplacian distribution was used as prior density.
Thus, it is possible to estimate the Fe+ and Na+ abundances for the nights presented here by using electron density profiles obtained using ISR data from Arecibo and prior in situ measurements of these ions.
Control selection was done by using incidence density sampling.
Neutrophils were isolated by using density gradients.
Likewise, by using density-dependent fractionation of genomic DNA prior to TRFLP profiling (GC-TRFLP) it is possible to greatly increase the information available from the community by decreasing the number of overlapping TRFs from unrelated bacteria.
For example in order to have closed form full conditionals we could use gamma prior densities.
We use the prior densities for branching times given a tree, derived in [ 24].
This is done by using the hypergeometric probability density function.
The state of continuing particles X k (c)i updates by the prior density of target.
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