A computer-implemented discrete region-based calling method, comprising: receiving a region delineated by a chromosome , a start marker and an end marker and for each marker within the region: dividing a logarithmic ratio for the marker by an expected responsiveness of the marker to copy number change to generate a phi-transformed logarithmic ratio for the marker ; modeling a probability distribution of the phi-transformed logarithmic ratio in one or more of states by calculating an emission probability for each of the states ; translating a local path window across the marker to determine a set of possible transition paths through the states and the markers ; constructing a local evidence matrix , for each of the markers , comprising a level of evidence for each of the states based on observed data; operating a Viterbi decoder with a transition matrix, the local evidence matrix and a state initial probability vector to generate a state vector comprising the state associated with each of the markers along the most probable path through the local evidence matrix; operating a first segmenter with the state vector to partition segments into a segmentation table of contiguous markers with the same state; operating a second segmenter with the segmentation table and a minimum segment size to merge segments below the minimum segment size into adjoining segments to produce a minimized segmentation table ; operating a segmentation finalizer on the minimized segmentation table to convert segment indexes to genomic positions ; and summarizing the phi-transformed logarithmic ratio within each segment using the segment's median; and generating a total per-marker value for each segment . dividing a logarithmic ratio for the marker by an expected responsiveness of the marker to copy number change to generate a phi-transformed logarithmic ratio for the marker ; modeling a probability distribution of the phi-transformed logarithmic ratio in one or more of states by calculating an emission probability for each of the states ; translating a local path window across the marker to determine a set of possible transition paths through the states and the markers ; constructing a local evidence matrix , for each of the markers , comprising a level of evidence for each of the states based on observed data; operating a Viterbi decoder with a transition matrix, the local evidence matrix and a state initial probability vector to generate a state vector comprising the state associated with each of the markers along the most probable path through the local evidence matrix; operating a first segmenter with the state vector to partition segments into a segmentation table of contiguous markers with the same state; operating a second segmenter with the segmentation table and a minimum segment size to merge segments below the minimum segment size into adjoining segments to produce a minimized segmentation table ; operating a segmentation finalizer on the minimized segmentation table to convert segment indexes to genomic positions ; and summarizing the phi-transformed logarithmic ratio within each segment using the segment's median; and generating a total per-marker value for each segment . <EOS>