Search Results - empirical ((((algorithmses OR algorithme) OR algorithms) OR algorithms) OR algorithms)

Computational materials discovery

Table of Contents: “…3.1.2 Empirical Interatomic Potentials3.1.3 Machine Learning Interatomic Potentials; 3.2 Simple Problem: Fitting of Potential Energy Surfaces; 3.2.1 Representation of Atomic Systems; 3.2.2 An Overview of Machine Learning Methods; 3.3 Machine Learning Interatomic Potentials; 3.3.1 Representation of Atomic Environments; 3.3.2 Existing MLIPs; 3.4 Fitting and Testing of Interatomic Potentials; 3.4.1 Optimization Algorithms; 3.4.2 Validation and Cross-validation; 3.4.3 Learning on the Fly; 3.5 Discussion; 3.5.1 Which Potential Is Better?…”
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Modeling semi-arid water-soil-vegetation systems by Wang, Xixi

Table of Contents: “…3.6 Topsoil erosion -- 3.6.1 Aeolian erosion -- 3.6.2 Fluvial erosion -- 3.6.3 Effects of physical and biological crusts on erosion -- 3.7 Summary and discussion -- References -- Chapter 4 Mathematical models -- 4.1 Overview -- 4.2 Comparisons of existing models -- 4.2.1 HYDRUS-1D -- 4.2.2 SWAT -- 4.2.3 SWAP -- 4.2.4 Comparisons -- 4.3 Model selection -- 4.4 Development of new algorithms -- 4.4.1 Physical crusts -- 4.4.2 Biocrusts -- 4.4.3 Low-moisture soils -- 4.4.4 Dry soil layers -- 4.4.5 The SWAP-E model -- 4.5 Measures of model performance -- 4.5.1 Empirical judgement…”
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Mechanisms and games for dynamic spectrum allocation

Table of Contents: “…7.3.10 Other equilibrium concepts -- 7.4 Learning equilibria -- 7.4.1 Learning Nash equilibria -- 7.4.2 Learning epsilon-equilibrium -- 7.4.3 Learning coarse correlated equilibrium -- 7.4.4 Learning satisfaction equilibrium -- 7.4.5 Discussion -- 7.5 Conclusion -- References -- II Cognitive radio and sharing of unlicensed spectrum -- 8 Cooperation in cognitiveradio networks: from accessto monitoring -- 8.1 Introduction -- 8.1.1 Cooperation in cognitive radio: mutual benefits and costs -- 8.2 An overview of coalitional game theory -- 8.3 Cooperative spectrum exploration and exploitation -- 8.3.1 Motivation -- 8.3.2 Basic problem -- 8.3.3 Joint sensing and access as a cooperative game -- 8.3.4 Coalition formation algorithm for joint sensing and access -- 8.3.5 Numerical results -- 8.4 Cooperative primary user activity monitoring -- 8.4.1 Motivation -- 8.4.2 Primary user activity monitoring: basic model -- 8.4.3 Cooperative primary user monitoring -- 8.4.4 Numerical results -- 8.5 Summary -- Acknowledgements -- Copyright notice -- References -- 9 Cooperative cognitive radios with diffusion networks -- 9.1 Introduction -- 9.2 Preliminaries -- 9.2.1 Basic tools in convex and matrix analysis -- 9.2.2 Graphs -- 9.3 Distributed spectrum sensing -- 9.4 Iterative consensus-based approaches -- 9.4.1 Average consensus algorithms -- 9.4.2 Acceleration techniques for iterative consensus algorithms -- 9.4.3 Empirical evaluation -- 9.5 Consensus techniques based on CoMAC -- 9.6 Adaptive distributed spectrum sensing based on adaptive subgradient techniques -- 9.6.1 Distributed detection with adaptive filters -- 9.6.2 Set-theoretic adaptive filters for distributed detection -- 9.6.3 Empirical evaluation -- 9.7 Channel probing -- 9.7.1 Introduction -- 9.7.2 Admissibility problem -- 9.7.3 Power and admission control algorithms.…”
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Multivariate statistics : proceedings of the 6th Tartu Conference, Tartu, Estonia, 19-22 August 1999

Table of Contents: “…s F-statistic: A simulation studyZero-boundary Voronoi partitions -- On expected values of fourth-degree matrix products of a multinormal matrix variate -- A multivariate Buckley-James estimator -- A new algorithm of the linear discriminant function using integer programming -- Robustification of “approximating approachâ€? …”
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Biomechanics : optimization, uncertainties and reliability by Kharmanda, Ghias, El Hami, Abdelkhalak

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Pattern recognition in industry by Bhagat, Phiroz

Table of Contents: “…Preface -- -- Acknowledgments -- About the Author -- <CENTER>Part I Philosophy</CENTER> -- -- CHAPTER 1: INTRODUCTION -- CHAPTER 2: PATTERNS WITHIN DATA -- CHAPTER 3: ADAPTING BIOLOGICAL PRINCIPLES FOR DEPLOYMENT IN COMPUTATIONAL SCIENCE -- CHAPTER 4: ISSUES IN PREDICTIVE EMPIRICAL MODELING -- <CENTER>Part II Technology</CENTER> -- CHAPTER 5: SUPERVISED LEARNINGCORRELATIVE NEURAL NETS -- CHAPTER 6: UNSUPERVISED LEARNING: AUTO-CLUSTERING AND SELF-ORGANIZING DATA -- CHAPTER 7: CUSTOMIZING FOR INDUSTRIAL STRENGTH APPLICATIONS -- CHAPTER 8: CHARACTERIZING AND CLASSIFYING TEXTUAL MATERIAL -- CHAPTER 9: PATTERN RECOGNITION IN TIME SERIES ANALYSIS -- CHAPTER 10: GENETIC ALGORITHMS -- <CENTER>Part III Case Studies</CENTER> -- CHAPTER 11: HARNESSING THE TECHNOLOGY FOR PROFITABILITY -- CHAPTER 12: REACTOR MODELING THROUGH IN SITU ADAPTIVE LEARNING -- CHAPTER 13: PREDICTING PLANT STACK EMISSIONS TO MEET ENVIRONMENTAL LIMITS -- CHAPTER 14: PREDICTING FOULING/COKING IN FIRED HEATERS -- CHAPTER 15: PREDICTING OPERATIONAL CREDITS -- CHAPTER 16: PILOT PLANT SCALE-UP BY INTERPRETING TRACER DIAGNOSTICS -- CHAPTER 17: PREDICTING DISTILLATION TOWER TEMPERATURES: MINING DATA FOR CAPTURING DISTINCT OPERATIONAL VARIABILITY -- CHAPTER 18: ENABLING NEW PROCESS DESIGN BASED ON LABORATORY DATA -- CHAPTER 19: FORECASTING PRICE CHANGES OF A COMPOSITE BASKET OF COMMODITIES -- CHAPTER 20: CORPORATE DEMOGRAPHIC TREND ANALYSIS -- EPILOGUE -- <CENTER>Appendices</CENTER> -- APPENDIX A1: THERMODYNAMICS AND INFORMATION THEORY -- APPENDIX A2: MODELING.…”
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Genomic Signal Processing. by Shmulevich, Ilya

Table of Contents: “…5.2 Complexity Regularization5.2.1 Regularization of the Error; 5.2.2 Structural Risk Minimization; 5.2.3 Empirical Complexity ; 5.3 Feature Selection; 5.3.1 Peaking Phenomenon; 5.3.2 Feature Selection Algorithms; 5.3.3 Impact of Error Estimation on Feature Selection; 5.3.4 Redundancy; 5.3.5 Parallel Incremental Feature Selection; 5.3.6 Bayesian Variable Selection; 5.4 Feature Extraction; Bibliography; 6 Clustering; 6.1 Examples of Clustering Algorithms; 6.1.1 Euclidean Distance Clustering; 6.1.2 Self-Organizing Maps; 6.1.3 Hierarchical Clustering; 6.1.4 Model-Based Cluster Operators.…”
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Robust Battery Management Systems. by Balasingam, Balakumar

Table of Contents: “…-- 1.4.1 Modularized Approach -- 1.4.2 Illustration of Algorithms Through Matlab Simulation…”
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Cluster analysis

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Progress in financial markets research

Table of Contents: “…Optimization of Technical Rules by Genetic Algorithms -- 8.5. Empirical Results -- Conclusion -- References -- Chapter 9: MODERN ANALYSIS OF FLUCTUATIONSIN FINANCIAL TIME SERIES AND BEYOND -- 9.1. …”
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Other geographies : the influences of Michael Watts

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Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models. by Holdaway, Keith R.

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Systemic Risk from Global Financial Derivatives. by Markose, Sheri M.

Table of Contents: “…Empirical (Small World) Core-Periphery Network Algorithm; 4. …”
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Discourse, of course : an overview of research in discourse studies

Table of Contents: “…/ Elisabeth Le -- Implicit and explicit coherence relations / Maite Taboada -- Style and culture in quantitative discourse analysis / Martin Kaltenbacher -- Devices of probability and obligation in text types / Xinzhang Yang -- Analysis and evaluation of argumentative discourse / Frans H. van Eemeren and Bart Garssen -- Embodied cognition, discourse, and dual coding theory : new directions / Mark Sadoski -- The cognition of discourse coherence / Ted Sanders and Wilbert Spooren -- A computational psycholinguistic algorithm to measure cohesion in discourse / Max M. …”
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Retail futures : the good, the bad and the ugly of the digital transformation

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Volatility Surface and Term Structure : High-profit Options Trading Strategies. by Lai, Kin Keung

Table of Contents: “…Cover; Title; Copyright; Contents; List of figures; List of tables; Preface; 1 Introduction; 1.1 Implied volatility; 1.2 Local volatility model; 1.3 Stochastic volatility model; 2 A novel model-free term structure for stock prediction; 2.1 Introduction; 2.2 Volatility model; 2.3 Model-free term structure; 2.4 Empirical tests; 2.5 Conclusions; 3 An adaptive correlation Heston model for stock prediction; 3.1 Introduction; 3.2 Adaptive correlation coefficient model; 3.3 Empirical tests; 3.4 Conclusions; 4 The algorithm to control risk using options; 4.1 Introduction.…”
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Computer-Aided Learning and Analysis for COVID-19 Disease. by Dhiman, Gaurav

Table of Contents: “…Cover -- Special issue (part 1) on computer-aided learning and analysis for COVID-19 disease -- COVID-19: risk prediction through nature inspired algorithm -- E-biomedical: a positive prospect to monitor human healthcare system using blockchain technology -- Pattern analysis: predicting COVID-19 pandemic in India using AutoML -- Predicting future diseases based on existing health status using link prediction -- Detection of COVID-19 cases through X-ray images using hybrid deep neural network -- Time series analysis of COVID-19 cases…”
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Handbook of monte carlo methods by Kroese, Dirk P., Taimre, Thomas, Botev, Zdravko I.

Table of Contents: “…Cover13; -- Contents -- Preface -- Acknowledgments -- 1 Uniform Random Number Generation -- 1.1 Random Numbers -- 1.1.1 Properties of a Good Random Number Generator -- 1.1.2 Choosing a Good Random Number Generator -- 1.2 Generators Based on Linear Recurrences -- 1.2.1 Linear Congruential Generators -- 1.2.2 Multiple-Recursive Generators -- 1.2.3 Matrix Congruential Generators -- 1.2.4 Modulo 2 Linear Generators -- 1.3 Combined Generators -- 1.4 Other Generators -- 1.5 Tests for Random Number Generators -- 1.5.1 Spectral Test -- 1.5.2 Empirical Tests -- References -- 2 Quasirandom Number Generation -- 2.1 Multidimensional Integration -- 2.2 Van der Corput and Digital Sequences -- 2.3 Halton Sequences -- 2.4 Faure Sequences -- 2.5 Sobol' Sequences -- 2.6 Lattice Methods -- 2.7 Randomization and Scrambling -- References -- 3 Random Variable Generation -- 3.1 Generic Algorithms Based on Common Transformations -- 3.1.1 Inverse-Transform Method -- 3.1.2 Other Transformation Methods -- 3.1.3 Table Lookup Method -- 3.1.4 Alias Method -- 3.1.5 Acceptance-Rejection Method -- 3.1.6 Ratio of Uniforms Method -- 3.2 Generation Methods for Multivariate Random Variables -- 3.2.1 Copulas -- 3.3 Generation Methods for Various Random Objects -- 3.3.1 Generating Order Statistics -- 3.3.2 Generating Uniform Random Vectors in a Simplex -- 3.3.3 Generating Random Vectors Uniformly Distributed in a Unit Hyperball and Hypersphere -- 3.3.4 Generating Random Vectors Uniformly Distributed in a Hyperellipsoid -- 3.3.5 Uniform Sampling on a Curve -- 3.3.6 Uniform Sampling on a Surface -- 3.3.7 Generating Random Permutations -- 3.3.8 Exact Sampling From a Conditional Bernoulli Distribution -- References -- 4 Probability Distributions -- 4.1 Discrete Distributions -- 4.1.1 Bernoulli Distribution -- 4.1.2 Binomial Distribution -- 4.1.3 Geometric Distribution -- 4.1.4 Hypergeometric Distribution -- 4.1.5 Negative Binomial Distribution -- 4.1.6 Phase-Type Distribution (Discrete Case) -- 4.1.7 Poisson Distribution -- 4.1.8 Uniform Distribution (Discrete Case) -- 4.2 Continuous Distributions -- 4.2.1 Beta Distribution -- 4.2.2 Cauchy Distribution -- 4.2.3 Exponential Distribution -- 4.2.4 F Distribution -- 4.2.5 Fr233;chet Distribution -- 4.2.6 Gamma Distribution -- 4.2.7 Gumbel Distribution -- 4.2.8 Laplace Distribution -- 4.2.9 Logistic Distribution -- 4.2.10 Log-Normal Distribution -- 4.2.11 Normal Distribution -- 4.2.12 Pareto Distribution -- 4.2.13 Phase-Type Distribution (Continuous Case) -- 4.2.14 Stable Distribution -- 4.2.15 Student's t Distribution -- 4.2.16 Uniform Distribution (Continuous Case) -- 4.2.17 Wald Distribution -- 4.2.18 Weibull Distribution -- 4.3 Multivariate Distributions -- 4.3.1 Dirichlet Distribution -- 4.3.2 Multinomial Distribution -- 4.3.3 Multivariate Normal Distribution -- 4.3.4 Multivariate Student's t Distribution -- 4.3.5 Wishart Distribution -- References -- 5 Random Process Generation -- 5.1 Gaussian Processes -- 5.1.1 Markovian Gaussian Processes -- 5.1.2 Stationary Gaussian Processes and the FFT -- 5.2 Markov Chains -- 5.3 Markov Jump Processes -- 5.4 Poisson Processes -- 5.4.1 Compound Poisson Process -- 5.5 Wiener Process and Brownian Motion -- 5.6 Stochastic Differential Eq.…”
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Qualitative Comparative Analysis : An Introduction To Research Design And Application. by Mello, Patrick A.

Table of Contents: “…Related Methods and Approaches -- Notes -- 9 QCA and Its Critics -- Analytical Robustness -- Comparisons with Other Methods -- Formalization and Algorithms -- Causal Analysis and Solution Terms -- Recognizing QCA's Strengths and Limitations -- Notes -- 10 Guiding Principles for QCA Research -- Good Research Practice -- Documenting and Communicating QCA Results -- QCA Resources -- Current Developments -- The Way Ahead -- Notes -- Appendix: Link to the Online R Manual -- Glossary -- A -- B -- C -- D -- E -- F -- G -- I -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- X-Y -- References…”
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Linkage Analysis and Gene Mapping. by WANG, Jiankang

Table of Contents: “…Theoretical Frequencies of 4 Homozygotes in Permanent Populations -- Genotypic Frequencies of Two Co-Dominant Loci in Temporary Populations -- Genotypic Frequencies of One Co-Dominant Locus and One Dominant Locus in Temporary Populations -- Genotypic Frequencies of One Co-Dominant Locus and One Recessive Locus in Temporary Populations -- Genotypic Frequencies of Two Dominant Loci in Temporary Populations -- Genotypic Frequencies of One Dominant Locus and One Recessive Locus in Temporary Populations -- Genotypic Frequencies of Two Recessive Loci in Temporary Populations -- Estimation of Two-Point Recombination Frequency -- Maximum Likelihood Estimation of Recombination Frequency in DH Populations -- General Procedure on the Maximum Likelihood Estimation of Recombination Frequency -- Estimation of Recombination Frequency Between One Co-Dominant and One Dominant Marker in F2 Population -- Initial Values in Newton Algorithm -- EM Algorithm in Estimating Recombination Frequency in F2 Populations -- Effects on the Estimation of Recombination Frequency from Segregation Distortion -- Exercises -- Three-Point Analysis and Linkage Map Construction -- Three-Point Analysis and Mapping Function -- Genetic Interference and Coefficient of Interference -- Mapping Function -- Construction of Genetic Linkage Maps -- Marker Grouping Algorithm -- Marker Ordering Algorithm -- Use of the k-Optimal Algorithm in Linkage Map Construction -- Rippling of the Ordered Markers -- Integration of Multiple Maps -- Comparison of the Recombination Frequency Estimation in Different Populations -- LOD Score in Testing the Linkage Relationship in Different Populations -- Accuracy of the Estimated Recombination Frequency -- Least Population Size to Declare the Significant Linkage Relationship and Close Linkage -- Linkage Analysis in Random Mating Populations.…”
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