Coding for Markov Sources: Course Held at the Department for Automation and Information June 1971 |
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Coding for Markov Sources: Course Held at the Department for Automation and ... Giuseppe Longo No preview available - 1980 |
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A₁ aperiodic asymptotic behaviour auxiliary distribution Auxiliary Lemmas binary sequence Channel capacity closed set Coder and Decoder coding conditional probabilities consequence Consider decreasing defined Discrete Memoryless Source Discrete Source disjoint sets distinct codewords divergence E₁ E₂ emits encoding rate entropy H Equivalence Classifier ergodic erroneous decoding Error Exponent error probability finite following theorem function given H₁ i-th row information loss Information Theory invariant p.d. irreducible law of large limiting behaviour log Pi lower bound Markov chain Markov Sources n-sequence Neyman-Pearson lemma overall probability P-probability P₁ Pi log positive numbers Prob probability distribution probability of erroneous self-information sequences of length Shannon Theorem Source Channel Source Coder Source output Source sequences stationary p.d. stochastic matrix Theorem 2.5 transmission errors transmission link true typical sequences Udine vector waveforms zero μ₁ μ₂ дај