Marginalized Beam Search Algorithms for Hierarchical HMMs
This addresses a specific inference bottleneck in bioinformatics and natural language processing, offering incremental improvements for domain applications.
The paper tackled the problem of inferring outer state sequences in Hierarchical Hidden Markov Models (HHMMs), where existing methods like Viterbi and Beam Search have limitations, and proposed two new algorithms (greedy marginalized BS and local focus BS) that approximate the most likely outer state sequence with higher performance than Viterbi, as evaluated on simulation and nanopore base calling data.
Inferring a state sequence from a sequence of measurements is a fundamental problem in bioinformatics and natural language processing. The Viterbi and the Beam Search (BS) algorithms are popular inference methods, but they have limitations when applied to Hierarchical Hidden Markov Models (HHMMs), where the interest lies in the outer state sequence. The Viterbi algorithm can not infer outer states without inner states, while the BS algorithm requires marginalization over prohibitively large state spaces. We propose two new algorithms to overcome these limitations: the greedy marginalized BS algorithm and the local focus BS algorithm. We show that they approximate the most likely outer state sequence with higher performance than the Viterbi algorithm, and we evaluate the performance of these algorithms on an explicit duration HMM with simulation and nanopore base calling data.