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In many applications, the ability to incorporate the state duration into the HMM is very important because conventional HMM‐based, Viterbi and Baum‐Welch algorithms are otherwise critically constrained in their modeling ability to distributions on state intervals that are geometric (this is shown in ssss1). This can lead to a significant decoding failure in noisy environments when the state‐interval distributions are not geometric (or approximately geometric). The starkest contrast occurs for multimodal distributions and heavy‐tailed distributions, the latter occurring for exon and intron length distributions (thus critical in gene finders). The hidden Markov model with binned duration (HMMBD) algorithm eliminates the HMM geometric distribution modeling constraint, as well as the HMMD maximum duration constraint, and offers a significant reduction in computational time for all HMMBD‐based methods to be approximately equal to the computational time of the HMM‐process alone.

ssss1 Edge feature enhancement via HMM/EM EVA filter. The filter “projects” via a Gaussian parameterization on emissions with variance boosted by the factor indicated. From prior publications by the author [1–3].