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In the context of OFDMA, the exclusion regions are to be established individually for each chunk [15].
Since the chunks have varying complexity, the agglomerative EM algorithm [27] is used to train GMM with the best amount of Gaussian components individually for each chunk.
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After breaking a customer's content into small chunks, Attributor creates digital fingerprints for each chunk.
Gaussian mixture models (GMMs) are trained for each chunk.
These researchers further group data items into a chunk and maintain one index table for each chunk.
We create one HMM state for each chunk.
Next, for each chunk, we construct an IBD Graph where the nodes are the chunks from each haplotype, the edges connect the haplotype chunks that are IBD to each other.
Once we obtain the top- k global configurations for all the chunks, we construct an HMM model where k values are created for each chunk.
Instead, we create one HMM state for each chunk, which significantly reduces the complexity of the HMM.
Condor-COPASI can split this task into parallel, using a non-overlapping range of parameter values for each chunk.
Imputation from the best-guess haplotypes was then carried out, for each chunk, using the aforementioned reference panels.
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