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== Extensions ==
A generalization of the Viterbi algorithm, termed the ''max-sum algorithm'' (or ''max-product algorithm'') can be used to find the most likely assignment of all or some subset of [[latent variable]]s in a large number of [[graphical model]]s, e.g. [[Bayesian network]]s, [[Markov random field]]s and [[conditional random field]]s. The latent variables need, in general, to be connected in a way somewhat similar to a [[hidden Markov model]] (HMM), with a limited number of connections between variables and some type of linear structure among the variables. The general algorithm involves ''message passing'' and is substantially similar to the [[belief propagation]] algorithm (which is the generalization of the [[forward-backward algorithm]]).▼
Zied Baklouti created a generalized Viterbi algorithm with transfer learning, creating a flexible algorithm based on external features<ref>[https://arxiv.org/abs/2308.05973 11 Aug 2023, Zied Baklouti: Tweet Sentiment Extraction using Viterbi Algorithm with Transfer Learning]</ref>. A theory of modern information theory is also introduced using experiments based on an improved Viterbi algorithm <ref>[https://arxiv.org/abs/2110.00433 6 Sep 2021, Zied Baklouti: External knowledge transfer deployment inside a simple double agent Viterbi algorithm]</ref>
▲A generalization of the Viterbi algorithm
With the algorithm called [[iterative Viterbi decoding]] one can find the subsequence of an observation that matches best (on average) to a given hidden Markov model. This algorithm is proposed by Qi Wang et al. to deal with [[turbo code]].<ref>{{cite journal |author1=Qi Wang |author2=Lei Wei |author3=Rodney A. Kennedy |year=2002 |title=Iterative Viterbi Decoding, Trellis Shaping, and Multilevel Structure for High-Rate Parity-Concatenated TCM |journal=IEEE Transactions on Communications |volume=50 |pages=48–55 |doi=10.1109/26.975743}}</ref> Iterative Viterbi decoding works by iteratively invoking a modified Viterbi algorithm, reestimating the score for a filler until convergence.
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