Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model

• المؤلف الرئيسي: Chan, Chin Tiong
• التنسيق: أطروحة
• اللغة: الإنجليزية
• منشور في: 2019
• الموضوعات: QA273-280 Probabilities. Mathematical statistics
• الوصول للمادة أونلاين: http://eprints.usm.my/61146/

Higher-order Hidden Markov model (HHMM) has a higher prediction accuracy than the first-order Hidden Markov model (HMM). This is due to more exploration of the historical state information for predicting the next state found in HHMM. State sequence for HHMM is invisible but the classical Viterbi algorithm is able to track the optimal state sequence. The extended entropy-based Viterbi algorithm is proposed for decoding HHMM. This algorithm is a memory-efficient algorithm due to its required memory space that is time independent. In other words, the required memory is not subjected to the length of the observational sequence. The entropybased Viterbi algorithm with a reduction approach (EVRA) is also introduced for decoding HHMM. The required memory of this algorithm is also time independent. In addition, the optimal state sequence obtained by the EVRA algorithm is the same as that obtained by the classical Viterbi algorithm for HHMM.