ALBERT

Description / Table of Contents: Contents: What is a Self-Organizing Map? - Applications of Different Self-Organizing Map Variants to Geographical Information Science . - An Integrated Exploratory Geovisualization Environment Based on Self-Organizing Map. - Visual Exploration of Spatial Interaction Data with Self-Organizing Maps. - Detecting Geographic Associations in English Dialect Features in North America within a Visual Data Mining Environment Integrating Self-Organizing . - Self-Organizing Maps for Density-Preserving Reduction of Objects in Cartographic Generalization. - Visualizing Human Movement in Attribute . - Climate Analysis, Modelling, and Regional Downscaling Using Self-Organizing. - Prototyping Broad-Scale Climate and Ecosystem Classes by Means of Self-Organising. - Self-Organising Map Principles Applied Towards Automating Road Extraction from Remotely Sensed Imagery . - Intelligent Systems for GIScience: Where Next? A GIScience Perspective

Description / Table of Contents: Contents: 1 Introduction to Quaternary Dating. 1.1 Introduction. 1.2 The development of Quaternary dating. 1.3 Precision and accuracy in dating. 1.4 Atomic structure, radioactivity and radiometric dating. 1.5 The Quaternary: stratigraphic framework and terminology. 1.6 The scope and content of the book. Notes. 2 Radiometric Dating 1: Radiocarbon Dating. 2.1 Introduction. 2.2 Basic principles. 2.3 Radiocarbon measurement. 2.4 Sources of error in radiocarbon dating. 2.5 Some problematic dating materials. 2.6 Calibration of the radiocarbon timescale. 2.7 Applications of radiocarbon dating. Notes. 3 Radiometric Dating 2: Dating using Long-lived and Short-lived Isotopes. 3.1 Introduction. 3.2 Potassium argon and Argon argon dating. 3.3 Uranium series dating. 3.4 Cosmogenic nuclide dating. 3.5 Dating using short-lived isotopes. Notes. 4 Radiometric Dating 3: Radiation Exposure Dating. 4.1 Introduction. 4.2 Luminescence dating. 4.3 Electron spin resonance dating. 4.4 Fission track dating.

Description / Table of Contents: Hydrothermal Properties of Materials: Experimental Data on Aqueous Phase Equilibria and Solution Properties at Elevated Temperatures and Pressures is designed for any scientists and engineers who deal with hydrothermal investigations and technologies. The book is organized into eight chapters, each dealing with a key physical property of behavior of solutions, so that a reader can obtain information on: hydrothermal experimental methods; available experimental data and the main features of properties behavior in a wide range of temperatures and pressures; and possible ways of experimental data processing for obtaining the derivative properties.

Description / Table of Contents: Preface. Acknowledgments. 1. Preliminaries 1. 1.1 Random Experiments. 1.2 Conditional Probability and Independence. 1.3 Random Variables and Probability Distributions. 1.4 Some Important Distributions. 1.5 Expectation. 1.6 Joint Distributions. 1.7 Functions of Random Variables. 1.8 Transforms. 1.9 Jointly Normal Random Variables. 1.10 Limit Theorems. 1.11 Poisson Processes. 1.12 Markov Processes. 1.12.1 Markov Chains. 1.12.2 Markov Jump Processes. 1.13 Efficiency of Estimators. 1.14 Information. 1.15 Convex Optimization and Duality. 1.15.1 Lagrangian Method. 1.15.2 Duality. Problems. References. 2. Random Number, Random Variable and Stochastic Process Generation. 2.1 Introduction. 2.2 Random Number Generation. 2.3 Random Variable Generation. 2.3.1 Inverse-Transform Method. 2.3.2 Alias Method. 2.3.3 Composition Method. 2.3.4 Acceptance-Rejection Method. 2.4 Generating From Commonly Used Distributions. 2.4.1 Generating Continuous Random Variables. 2.4.2 Generating Discrete Random Variables. 2.5 Random Vector Generation. 2.5.1 Vector Acceptance-Rejection Method. 2.5.2 Generating Variables From a Multinormal Distribution. 2.5.3 Generating Uniform Random Vectors Over a Simplex. 2.5.4 Generating Random Vectors, Uniformly Distributed Over a Unit Hyper-Ball and Hyper-Sphere. 2.5.5 Generating Random Vectors, Uniformly Distributed Over a Hyper-Ellipsoid. 2.6 Generating Poisson Processes. 2.7 Generating Markov Chains and Markov Jump Processes. 2.8 Generating Random Permutations. Problems. References. 3. Simulation of Discrete Event Systems. 3.1 Simulation Models. 3.2 Simulation Clock and Event List for DEDS. 3.3 Discrete Event Simulation. 3.3.1 Tandem Queue. 3.3.2 Repairman Problem. Problems. References. 4. Statistical Analysis of Discrete Event Systems. 4.1 Introduction. 4.2 Static Simulation Models. 4.3 Dynamic Simulation Models. 4.3.1 Finite-Horizon Simulation. 4.3.2 Steady-State Simulation. 4.4 The Bootstrap Method. Problems. References. 5. Controlling the Variance. 5.1 Introduction. 5.2 Common and Antithetic Random Variables. 5.3 Control Variables. 5.4 Conditional Monte Carlo. 5.4.1 Variance Reduction for Reliability Models. 5.5 Stratified Sampling. 5.6 Importance Sampling. 5.6.1 The Variance Minimization Method. 5.6.2 The Cross-Entropy Method. 5.7 Sequential Importance Sampling. 5.7.1 Non-linear Filtering for Hidden Markov Models. 5.8 The Transform Likelihood Ratio Method. 5.9 Preventing the Degeneracy of Importance Sampling. 5.9.1 The Two-Stage Screening Algorithm. 5.9.2 Case Study. Problems. References. 6. Markov Chain Monte Carlo. 6.1 Introduction. 6.2 The Metropolis-Hastings Algorithm. 6.3 The Hit-and-Run Sampler. 6.4 The Gibbs Sampler. 6.5 Ising and Potts Models. 6.6 Bayesian Statistics. 6.7 Other Markov Samplers. 6.8 Simulated Annealing. 6.9 Perfect Sampling. Problems. References. 7. Sensitivity Analysis and Monte Carlo Optimization. 7.1 Introduction. 7.2 The Score Function Method for Sensitivity Analysis of DESS. 7.3 Simulation-Based Optimization of DESS. 7.3.1 Stochastic Approximation. 7.3.2 The Stochastic Counterpart Method. 7.4 Sensitivity Analysis of DEDS. Problems. References. 8. The Cross-Entropy Method. 8.1 Introduction. 8.2 Estimation of Rare Event Probabilities. 8.2.1 The Root-Finding Problem. 8.2.2 The Screening Method for Rare Events. 8.3 The CE-Method for Optimization. 8.4 The Max-cut Problem. 8.5 The Partition Problem. 8.6 The Travelling Salesman Problem. 8.6.1 Incomplete Graphs. 8.6.2 Node Placement. 8.6.3 Case Studies. 8.7 Continuous Optimization. 8.8 Noisy Optimization. Problems. References. 9. Counting via Monte Carlo. 9.1 Counting Problems. 9.2 Satisfiability Problem. 9.2.1 Random K-SAT (K-RSAT). 9.3 The Rare-Event Framework for Counting. 9.3.1 Rare-Events for the Satisfiability Problem. 9.4 Other Randomized Algorithms for Counting. 9.4.1 Complexity of Randomized Algorithms: FPRAS and FPAUS. 9.5 MinxEnt and Parametric MinxEnt. 9.5.1 The MinxEnt Method. 9.5.2 Rare-Event Probability Estimation Using PME. 9.6 PME for COPs and Decision Making. 9.7 Numerical Results. Problems. References. Appendix A. A.1 Cholesky Square Root Method. A.2 Exact Sampling from a Conditional Bernoulli Distribution. A.3 Exponential Families. A.4 Sensitivity Analysis. A.4.1 Convexity Results. A.4.2 Monotonicity Results. A.5 A simple implementation of the CE algorithm for optimizing the 'peaks' function. A.6 Discrete-time Kalman Filter. A.7 Bernoulli Disruption Problem. A.8 Complexity of Stochastic Programming Problems. Problems. References. Acronyms. List of Symbols. Index.