Preface. - Notation. - 1. Error Source Models. - 1. 1 Description of Error Sources by Hidden Markov Models. - 1. 2 Binary Symmetric Stationary Channel. - 1. 3 Error Source Description by Matrix Processes. - 1. 4 Error Source Description by Semi-Markov Processes. - 1. 5 Some Particular Error Source Models. - 1. 6 Conclusion. - References. - 2. Matrix Probabilities. - 2. 1 Matrix Probabilities and Their Properties. - 2. 2 Matrix Transforms. - 2. 3 Matrix Distributions. - 2. 4 Markov Functions. - 2. 5 Monte Carlo Method. - 2. 6 Computing Scalar Probabilities. - 2. 7 Conclusion. - References. - 3. Model Parameter Estimation. - 3. 1 The Em Algorithm. - 3. 2 Baum-Welch Algorithm. - 3. 3 Markov Renewal Process. - 3. 4 Matrix-Geometric Distribution Parameter Estimation. - 3. 5 Matrix Process Parameter Estimation. - 3. 6 Hmm Parameter Estimation. - 3. 7 Monte Carlo Method of Model Building. - 3. 8 Error Source Model in Several Channels. - 3. 9 Conclusion. - References. - 4. Performance of Forward Error-Correction Systems. - 4. 1 Basic Characteristics of One-Way Systems. - 4. 2 Elements of Error-Correcting Coding. - 4. 3 Maximum A Posteriori Decoding. - 4. 4 Block Code Performance Characterization. - 4. 5 Convolutional Code Performance. - 4. 6 Computer Simulation. - 4. 7 Zero-Redundancy Codes. - 4. 8 Conclusion. - References. - 5. Performance Analysis of Communication Protocol. - 5. 1 Basic Characteristics of Two-Way Systems. - 5. 2 Return-Channel Messages. - 5. 3 Synchronization. - 5. 4 Arq Performance Characteristics. - 5. 5 Delay-Constained Systems. - 5. 6 Conclusion. - References. - 6. Continuous Time Hmm. - 6. 1 Continuous and Discrete Time Hmm. - 6. 2 Fitting Continuous Time Hmm. - 6. 3 Conclusion. - References. - 7. Continuous State Hmm. - 7. 1 Continuous and Discrete State Hmm. - 7. 2 Operator Probability. - 7. 3 Filtering Prediction and Smoothing. - 7. 4 Linear Systems. - 7. 5 Autoregressive Moving Average Processes. - 7. 6 Parameter Estimation. - 7. 7 Arma Channel Modeling. - 7. 8 Conclusion. - References. - Appendix 1. - 1. 1 Matrix Processes. - 1. 2 Markov Lumpable Chains. - 1. 3 Semi-Markov Lumpable Chains. - References. - Appendix 2. - 2. 1 Asymptotic Expansion of Matrix Probabilities. - 2. 2 Chernoff Bounds. - 2. 3 Block Graphs. - References. - Appendix 3. - 3. 1 Statistical Inference. - 3. 2 Markov Chain Model Building. - 3. 3 Semi-Markov Process Hypothesis Testing. - 3. 4 Matrix Process Parameter Estimation. - References. - Appendix 4. - 4. 1 Sums With Binomial Coefficients. - 4. 2 Maximum-Distance-Separable Code Weight Generating Function. - 4. 3 Union Bounds on Viterbi Algorithm Performance. - References. - Appendix 5. - 5. 1 Matrices. - References. - Appendix 6. - 6. 1 Markov Chains and Graphs. - References. - Appendix 7. - 7. 1 Markov Processes. - 7. 2 Gauss-Markov Processes. - References. Language: English