A Novel Entropy-Based Decoding Algorithm for a Generalized High-Order Discrete Hidden Markov Model

<div>The evolution of the trellis structure for a transformation of a second-order into an equivalent first-order HMM with the observation sequence <span class="nowrap"><svg height="12.5794pt" id="M420" style="vertical-align:-3.29107pt" version="1.1" viewbox="-0.0498162 -9.28833 287.568 12.5794" width="287.568pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M445 282C445 390 384 448 297 448C266 448 231 437 189 418C96 376 23 279 23 151C23 65 66 -12 169 -12C233 -12 294 18 337 53C411 113 445 201 445 282ZM355 280C355 161 302 62 243 36C231 31 219 30 207 30C147 30 111 78 111 154C111 302 180 380 220 399C231 404 246 407 264 407C311 407 355 367 355 280Z" id="g113-112"></path></g><g transform="matrix(.0091,0,0,-0.0091,6.019,3.132)"><path d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z" id="g50-50"></path></g><g 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Journal of Probability and Statistics

fig5

Figure 5

Figure 5: A Novel Entropy-Based Decoding Algorithm for a Generalized High-Order Discrete Hidden Markov Model 