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Stochastic Algorithms: Foundations and Applications

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Table of Contents

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    Book Overview
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    Chapter 1 Scenario Reduction Techniques in Stochastic Programming
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    Chapter 2 Statistical Learning of Probabilistic BDDs
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    Chapter 3 Learning Volatility of Discrete Time Series Using Prediction with Expert Advice
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    Chapter 4 Prediction of Long-Range Dependent Time Series Data with Performance Guarantee
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    Chapter 5 Bipartite Graph Representation of Multiple Decision Table Classifiers
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    Chapter 6 Bounds for Multistage Stochastic Programs Using Supervised Learning Strategies
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    Chapter 7 On Evolvability: The Swapping Algorithm, Product Distributions, and Covariance
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    Chapter 8 A Generic Algorithm for Approximately Solving Stochastic Graph Optimization Problems
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    Chapter 9 How to Design a Linear Cover Time Random Walk on a Finite Graph
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    Chapter 10 Propagation Connectivity of Random Hypergraphs
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    Chapter 11 Graph Embedding through Random Walk for Shortest Paths Problems
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    Chapter 12 Relational Properties Expressible with One Universal Quantifier Are Testable
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    Chapter 13 Theoretical Analysis of Local Search in Software Testing
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    Chapter 14 Firefly Algorithms for Multimodal Optimization
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    Chapter 15 Economical Caching with Stochastic Prices
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    Chapter 16 Markov Modelling of Mitochondrial BAK Activation Kinetics during Apoptosis
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    Chapter 17 Stochastic Dynamics of Logistic Tumor Growth
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