Uses Markov Chain Monte Carlo (MCMC) and Gibbs sampling to estimate the posterior probability of being in one of six Copy Number Variation states (states: 0, 0.5, 1, 1.5, 2, 3) for CNV's identified by inferCNV's HMM. Posterior probabilities are found for the entire CNV cluster and each individual cell line in the CNV.

`bugs_model`

BUGS model.

`sig`

fitted values for cell lines, 1/standard deviation to be used for determining the distribution of each cell line

`mu`

Mean values to be used for determining the distribution of each cell line

`group_id`

ID's given to the cell clusters.

`cell_gene`

List containing the Cells and Genes that make up each CNV.

`cnv_probabilities`

Probabilities of each CNV belonging to a particular state from 0 (least likely)to 1 (most likely).

`cell_probabilities`

Probabilities of each cell being in a particular state, from 0 (least likely)to 1 (most likely).

`args`

Input arguments given by the user

`cnv_regions`

ID for each CNV found by the HMM

`States`

States that are identified and (depending on posterior MCMC input methods) modified.

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