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| Series Foreword | |
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| Preface | |
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| Introduction | |
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| Biological Data in Digital Symbol Sequences | |
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| Genomes--Diversity, Size, and Structure | |
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| Proteins and Proteomes | |
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| On the Information Content of Biological Sequences | |
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| Prediction of Molecular Function and Structure | |
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| Machine Learning Foundations: The Probabilistic Framework | |
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| Introduction: Bayesian Modeling | |
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| The Cox-Jaynes Axioms | |
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| Bayesian Inference and Induction | |
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| Model Structures: Graphical Models and Other Tricks | |
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| Summary | |
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| Probabilistic Modeling and Inference: Examples | |
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| The Simplest Sequence Models | |
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| Statistical Mechanics | |
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| Machine Learning Algorithms | |
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| Introduction | |
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| Dynamic Programming | |
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| Gradient Descent | |
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| EM/GEM Algorithms | |
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| Markov Chain Monte Carlo Methods | |
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| Simulated Annealing | |
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| Evolutionary and Genetic Algorithms | |
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| Learning Algorithms: Miscellaneous Aspects | |
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| Neural Networks: The Theory | |
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| Introduction | |
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| Universal Approximation Properties | |
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| Priors and Likelihoods | |
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| Learning Algorithms: Backpropagation | |
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| Neural Networks: Applications | |
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| Sequence Encoding and Output Interpretation | |
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| Prediction of Protein Secondary Structure | |
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| Prediction of Signal Peptides and Their Cleavage Sites | |
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| Applications for DNA and RNA Nucleotide Sequences | |
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| Hidden Markov Models: The Theory | |
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| Introduction | |
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| Prior Information and Initialization | |
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| Likelihood and Basic Algorithms | |
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| Learning Algorithms | |
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| Applications of HMMs: General Aspects | |
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| Hidden Markov Models: Applications | |
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| Protein Applications | |
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| DNA and RNA Applications | |
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| Conclusion: Advantages and Limitations of HMMs | |
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| Hybrid Systems: Hidden Markov Models and Neural Networks | |
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| Introduction to Hybrid Models | |
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| The Single-Model Case | |
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| The Multiple-Model Case | |
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| Simulation Results | |
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| Summary | |
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| Probabilistic Models of Evolution: Phylogenetic Trees | |
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| Introduction to Probabilistic Models of Evolution | |
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| Substitution Probabilities and Evolutionary Rates | |
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| Rates of Evolution | |
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| Data Likelihood | |
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| Optimal Trees and Learning | |
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| Parsimony | |
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| Extensions | |
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| Stochastic Grammars and Linguistics | |
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| Introduction to Formal Grammars | |
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| Formal Grammars and the Chomsky Hierarchy | |
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| Applications of Grammars to Biological Sequences | |
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| Prior Information and Initialization | |
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| Likelihood | |
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| Learning Algorithms | |
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| Applications of SCFGs | |
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| Experiments | |
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| Future Directions | |
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| Internet Resources and Public Databases | |
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| A Rapidly Changing Set of Resources | |
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| Databases over Databases and Tools | |
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| Databases over Databases | |
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| Databases | |
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| Sequence Similarity Searches | |
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| Alignment | |
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| Selected Prediction Servers | |
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| Molecular Biology Software Links | |
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| Ph.D. Courses over the Internet | |
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| HMM/NN Simulator | |
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| Statistics | |
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| Decision Theory and Loss Functions | |
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| Quadratic Loss Functions | |
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| The Bias/Variance Trade-off | |
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| Combining Estimators | |
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| Error Bars | |
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| Sufficient Statistics | |
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| Exponential Family | |
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| Gaussian Process Models | |
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| Variational Methods | |
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| Information Theory, Entropy, and Relative Entropy | |
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| Entropy | |
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| Relative Entropy | |
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| Mutual Information | |
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| Jensen's Inequality | |
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| Maximum Entropy | |
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| Minimum Relative Entropy | |
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| Probabilistic Graphical Models | |
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| Notation and Preliminaries | |
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| The Undirected Case: Markov Random Fields | |
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| The Directed Case: Bayesian Networks | |
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| HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures | |
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| Scaling | |
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| Periodic Architectures | |
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| State Functions: Bendability | |
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| Dirichlet Mixtures | |
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| List of Main Symbols and Abbreviations | |
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| References | |
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| Index | |