“…New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. …”

“…Filling a particular gap in the literature on intra-Asian trade prior to the twentieth century, this is an insightful study that makes a considerable contribution to our knowledge of the Asian trade both prior to, and after, the arrival of colonial states. …”

Tabla de Contenidos: “…15 Cooperative Cognitive Communications 613 -- 15.1 Introduction, 613 -- 15.2 Cooperative Spectrum Sensing, 619 -- 15.3 Cooperative Spectrum Sensing and Channel-Access Decisions, 621 -- 15.4 Cooperative Communications Strategies in Cognitive Radio Networks, 624 -- 15.5 Asymmetric Cooperative Relaying in DSA Cognitive Radios, 627 -- 15.5.1 Secondary User Optimal Power Allocation for Asymmetric Cooperative Relaying, 629 -- 15.5.2 Centralized Assignment of Cognitive Radios for Cooperation with Primary Users: An Ideal Approach, 635 -- 15.5.3 Centralized Assignment of Cognitive Radios for Cooperation with Primary Users: A Realistic Approach, 640 -- 15.6 Summary, 644 -- 16 Machine Learning in Cognitive Radios 647 -- 16.1 Introduction, 647 -- 16.2 Artificial Neural Networks, 650 -- 16.2.1 Learning Algorithms for LTUs, 651 -- 16.2.2 Layered Neural Networks, 655 -- 16.2.3 Learning in Layered Feed-Forward Networks: Back-Propagation Algorithm, 656 -- 16.2.4 Neural Networks in Cognitive Radios, 662 -- 16.3 Support Vector Machines, 664 -- 16.3.1 Statistical Learning Theory, 665 -- 16.3.2 Structural Risk Minimization with Support Vector Machines, 669 -- 16.3.3 Linear Support Vector Machines, 670 -- 16.3.4 Nonlinear Support Vector Machines, 674 -- 16.3.5 Kernel Function Implementation of Support Vector Machines, 677 -- 16.3.6 SVMs in Cognitive Radios, 679 -- 16.4 Reinforcement Learning, 681 -- 16.4.1 Temporal Difference Learning, 683 -- 16.4.2 Q-Learning in a POMDP: Replicated Q-Learning, 684 -- 16.4.3 Reinforcement Learning in Cognitive Radios, 686 -- 16.5 Multiagent Learning, 688 -- 16.5.1 Game-Theoretic Multiagent Learning, 691 -- 16.5.2 Cooperative Multiagent Learning, 694 -- 16.5.3 Multiagent Learning in Cognitive Radio Networks, 696 -- 16.6 Summary, 698 -- Appendix A Nyquist Sampling Theorem 704 -- Appendix B A Collection of Useful Probability Distributions 711 -- B.1 Univariate Distributions, 711 -- B.2 Multivariate Distributions, 713 -- Appendix C Conjugate Priors 716 -- REFERENCES 721.…”

Tabla de Contenidos: “…4.3.3 Results and Discussion 129 -- 4.4 Phase Spectrum of the Quaternion Fourier Transform Approach 131 -- 4.4.1 Biological Plausibility for Multichannel Representation 131 -- 4.4.2 Quaternion and Its Properties 132 -- 4.4.3 Phase Spectrum of Quaternion Fourier Transform (PQFT) 134 -- 4.4.4 Results Comparison 138 -- 4.4.5 Dynamic Saliency Detection of PQFT 140 -- 4.5 Pulsed Discrete Cosine Transform Approach 141 -- 4.5.1 Approach of Pulsed Principal Components Analysis 141 -- 4.5.2 Approach of the Pulsed Discrete Cosine Transform 143 -- 4.5.3 Multichannel PCT Model 144 -- 4.6 Divisive Normalization Model in the Frequency Domain 145 -- 4.6.1 Equivalent Processes with a Spatial Model in the Frequency Domain 146 -- 4.6.2 FDN Algorithm 149 -- 4.6.3 Patch FDN 150 -- 4.7 Amplitude Spectrum of Quaternion Fourier Transform (AQFT) Approach 152 -- 4.7.1 Saliency Value for Each Image Patch 152 -- 4.7.2 The Amplitude Spectrum for Each Image Patch 153 -- 4.7.3 Differences between Image Patches and their Weighting to Saliency Value 154 -- 4.7.4 Patch Size and Scale for Final Saliency Value 156 -- 4.8 Modelling from a Bit-stream 157 -- 4.8.1 Feature Extraction from a JPEG Bit-stream 157 -- 4.8.2 Saliency Detection in the Compressed Domain 160 -- 4.9 Further Discussions of Frequency Domain Approach 161 -- References 163 -- 5 Computational Models for Top-down Visual Attention 167 -- 5.1 Attention of Population-based Inference 168 -- 5.1.1 Features in Population Codes 170 -- 5.1.2 Initial Conspicuity Values 171 -- 5.1.3 Updating and Transformation of Conspicuity Values 173 -- 5.2 Hierarchical Object Search with Top-down Instructions 175 -- 5.2.1 Perceptual Grouping 175 -- 5.2.2 Grouping-based Salience from Bottom-up Information 176 -- 5.2.3 Top-down Instructions and Integrated Competition 179 -- 5.2.4 Hierarchical Selection from Top-down Instruction 179 -- 5.3 Computational Model under Top-down Influence 180 -- 5.3.1 Bottom-up Low-level Feature Computation 181 -- 5.3.2 Representation of Prior Knowledge 181.…”