Search Results - (( empirical (algorithmics OR (algorithmic OR algorithm ))) OR ( empirical algorithms ))

Pattern recognition : a quality of data perspective by Homenda, Władysław, Pedrycz, Witold, 1953-

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A practitioner's guide to asset allocation by Kinlaw, William, Kinlaw, William, Kritzman, Mark P., Turkington, David, 1983-

Table of Contents: “…-- Linear Hedging Strategies -- Nonlinear Hedging Strategies -- Economic Intuition -- References -- Notes -- Chapter 11: Illiquidity -- The Challenge -- Shadow Assets and Liabilities -- Expected Return and Risk of Shadow Allocations -- Other Considerations -- Case Study -- The Bottom Line -- Appendix -- References -- Notes -- Chapter 12: Risk in the Real World -- The Challenge -- End-of-Horizon Exposure to Loss -- Within-Horizon Exposure to Loss -- Regimes -- The Bottom Line -- References -- Notes -- Chapter 13: Estimation Error -- The Challenge -- Traditional Approaches to Estimation Error -- Stability-Adjusted Optimization -- Building a Stability-Adjusted Return Distribution -- Determining the Optimal Allocation -- Empirical Analysis -- The Bottom Line -- References -- Notes -- Chapter 14: Leverage versus Concentration -- The Challenge -- Leverage in Theory -- Leverage in Practice -- The Bottom Line -- References -- Notes -- Chapter 15: Rebalancing -- The Challenge -- The Dynamic Programming Solution -- The Markowitz-van Dijk Heuristic -- The Bottom Line -- References -- Notes -- Chapter 16: Regime Shifts -- The Challenge -- Predictability of Return and Risk -- Regime-Sensitive Allocation -- Tactical Asset Allocation -- The Bottom Line -- Appendix: Baum-Welch Algorithm -- References -- Notes -- Section Four: Addendum -- Chapter 17: Key Takeaways.…”
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Stochastic modelling of electricity and related markets by Benth, Fred Espen, 1969-

Table of Contents: “…Maximum smooth forward curve. 7.3. Putting the algorithm to work -- 8. Modelling of the electricity futures market. 8.1. …”
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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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Dynamic factor models

Table of Contents: “…The FECM Form for Structural Analysis; 3. Data and empirical applications; 4. Forecasting macroeconomic variables; 4.1. …”
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Extreme events : observations, modeling, and economics

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Probability, Finance and Insurance : Proceedings of a Workshop. by Lai, T. L.

Table of Contents: “…Summary; References; Phase changes in random recursive structures and algorithms; 1. Phase changes related to the Poisson distribution; 2. …”
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Computational materials engineering : achieving high accuracy and efficiency in metals processing simulations by Pietrzyk, Maciej, 1947-, Madej, Łukasz, Rauch, Lukasz, Szeliga, Danuta

Table of Contents: “…Toward Increase of the Efficiency of Modeling -- 2.1 Improvement of Numerical Algorithms -- 2.1.1 Metamodeling -- 2.1.2 Inverse analysis -- 2.1.2.1 General formulation of the inverse problem -- 2.1.2.2 Regularization -- 2.1.2.3 Methods of regularizations -- 2.1.2.4 Numerical computations and regularization in the finite-dimension setting -- 2.1.3 Sensitivity analysis -- 2.1.3.1 Local SA -- 2.1.3.2 Global SA -- 2.1.3.3 The implementation of SA algorithms -- 2.1.3.4 A strategy for the identification of the model parameters -- 2.2 Improvement of Hardware -- 2.2.1 General idea of high-performance computing -- 2.2.2 Development of clusters -- 2.2.3 Development of heterogeneous architectures -- 2.2.4 Development of grid environments -- 3. …”
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Evidencing the Productive Value of the 4IR in the AEC Sector. by Ejohwomu, Obuks A.

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Handbook of high-frequency trading and modeling in finance

Table of Contents: “…4.1 Introduction4.2 Background; 4.2.1 Portfolios And Optimization; 4.2.2 Algorithmic Complexity; 4.2.3 Performance; 4.2.4 Ising Model; 4.2.5 Adiabatic Quantum Computing; 4.3 The models; 4.3.1 Financial Model; 4.3.2 Graph-Theoretic Combinatorial Optimization Models; 4.3.3 Ising And Qubo Models; 4.3.4 Mixed Models; 4.4 Methods; 4.4.1 Model Implementation; 4.4.2 Input Data; 4.4.3 Mean-Variance Calculations; 4.4.4 Implementing The Risk Measure; 4.4.5 Implementation Mapping; 4.5 Results; 4.5.1 The Simple Correlation Model; 4.5.2 The Restricted Minimum-Risk Model.…”
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Inside the black box : a simple guide to quantitative and high frequency trading by Narang, Rishi K, 1974-

Table of Contents: “…Final Thoughts on OptimizationOutput of Portfolio Construction Models; How Quants Choose a Portfolio Construction Model; Summary; Notes; Chapter 7 Execution; Order Execution Algorithms; Aggressive versus Passive; Other Order Types; Large Order versus Small Order; Where to Send an Order; Trading Infrastructure; Summary; Notes; Chapter 8 Data; The Importance of Data; Types of Data; Sources of Data; Cleaning Data; Storing Data; Summary; Notes; Chapter 9 Research; Blueprint for Research: The Scientific Method; Idea Generation; Testing; In-Sample Testing, a.k.a. …”
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Data Analysis and Applications 1 : New and Classical Approaches. by Skiadas, Christos

Table of Contents: “…From statistical learning theory to empirical validation…”
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Romance syntax, semantics and L2 acquisition : selected papers from the 30th Linguistic Symposium on Romance Languages : Gainesville, Florida, February 2000

Table of Contents: “…Discussion -- 6.1 The learning algorithm -- 6.2 Conceptualizing Learning.…”
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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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Neural control engineering : the emerging intersection between control theory and neuroscience by Schiff, Steven J.

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Essentials of Monte Carlo Simulation : Statistical Methods for Building Simulation Models. by Thomopoulos, Nick T.

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Qualitative Spatial and Temporal Reasoning. by Ligozat, Gérard

Table of Contents: “…Ladkin and Reinefeld's algorithm; 2.4.2. Empirical study of the consistency problem; 2.5. …”
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Artificial intelligence research and development : current challenges, new trends and applications

Table of Contents: “…-- An Argumentation Approach for Agreement Analysis in Reddit Debates -- Tweet Sentiment Visualization and Classification Using Manifold Dimensionality Reduction -- N-Channel Convolutional Neural Networks for Irony Detection in Twitter -- A New Algorithm for Speech Enhancement Based on Multivariate Empirical Mode Decomposition -- Classifying and Generalizing Successful Parameter Combinations for Sound Design -- A Visual Distance for WordNet -- Enhancing Text Spotting with a Language Model and Visual Context Information -- Cognitive Systems and Agents -- What Is the Physics of Intelligence? …”
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Data Mining and Statistics for Decision Making by Tufféry, Stéphane, Tufféry, Stéphane

Table of Contents: “…Learning algorithms -- 8.7. The main neural networks -- Automatic clustering methods -- 9.1. …”
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Discrete choice experiments in marketing : use of priors in efficient choice designs and their application to individual preference measurement by Zwerina, Klaus, 1967-

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