Search Results - empirical (algorithms OR ((algorithm OR (algorythmic OR algorithmssic)) OR algorithms))

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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Intelligent computational systems : a multi-disciplinary perspective

Table of Contents: “…3.2.1. Genetic Algorithm (GA) -- 3.2.2. Particle Swarm Optimization Algorithm (PSO) -- 3.3. …”
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Big data and differential privacy : analysis strategies for railway track engineering by Attoh-Okine, Nii O.

Table of Contents: “…3.12.2.2 Deep Belief Nets (DBN)3.12.3 Convolutional Neural Networks (CNN); 3.12.4 Granular Computing (Rough Set Theory); 3.12.5 Clustering; 3.12.5.1 Measures of Similarity or Dissimilarity; 3.12.5.2 Hierarchical Methods; 3.12.5.3 Non-Hierarchical Clustering; 3.12.5.4 k-Means Algorithm; 3.12.5.5 Expectation-Maximization (EM) Algorithms; 3.13 Data Stream Processing; 3.13.1 Methods and Analysis; 3.13.2 LogLog Counting; 3.13.3 Count-Min Sketch; 3.13.3.1 Online Support Regression; 3.14 Remarks; References; Chapter 4 Basic Foundations of Big Data; 4.1 Introduction; 4.2 Query.…”
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Mathematics of evolution and phylogeny

Table of Contents: “…List of Contributors -- 1 The minimum evolution distance-based approach of phylogenetic inference -- 1.1 Introduction -- 1.2 Tree metrics -- 1.2.1 Notation and basics -- 1.2.2 Three-point and four-point conditions -- 1.2.3 Linear decomposition into split metrics -- 1.2.4 Topological matrices -- 1.2.5 Unweighted and balanced averages -- 1.2.6 Alternate balanced basis for tree metrics -- 1.2.7 Tree metric inference in phylogenetics -- 1.3 Edge and tree length estimation -- 1.3.1 The LS approach -- 1.3.2 Edge length formulae -- 1.3.3 Tree length formulae -- 1.3.4 The positivity constraint -- 1.3.5 The balanced scheme of Pauplin -- 1.3.6 Semple and Steel combinatorial interpretation -- 1.3.7 BME: a WLS interpretation -- 1.4 The agglomerative approach -- 1.4.1 UPGMA and WPGMA -- 1.4.2 NJ as a balanced minimum evolution algorithm -- 1.4.3 Other agglomerative algorithms -- 1.5 Iterative topology searching and tree building -- 1.5.1 Topology transformations.; 1.5.2 A fast algorithm for NNIs with OLS -- 1.5.3 A fast algorithm for NNIs with BME -- 1.5.4 Iterative tree building with OLS -- 1.5.5 From OLS to BME -- 1.6 Statistical consistency -- 1.6.1 Positive results -- 1.6.2 Negative results -- 1.6.3 Atteson's safety radius analysis -- 1.7 Discussion -- Acknowledgements -- 2 Likelihood calculation in molecular phylogenetics -- 2.1 Introduction -- 2.2 Markov models of sequence evolution -- 2.2.1 Independence of sites -- 2.2.2 Setting up the basic model -- 2.2.3 Stationary distribution -- 2.2.4 Time reversibility -- 2.2.5 Rate of mutation -- 2.2.6 Probability of sequence evolution on a tree -- 2.3 Likelihood calculation: the basic algorithm -- 2.4 Likelihood calculation: improved models -- 2.4.1 Choosing the rate matrix -- 2.4.2 Among site rate variation -- 2.4.3 Site-specific rate variation -- 2.4.4 Correlated evolution between sites -- 2.5 Optimizing parameters -- 2.5.1 Optimizing continuous parameters -- 2.5.2 Searching for the optimal tree.; 2.5.3 Alternative search strategies -- 2.6 Consistency of the likelihood approach -- 2.6.1 Statistical consistency -- 2.6.2 Identifiability of the phylogenetic models -- 2.6.3 Coping with errors in the model -- 2.7 Likelihood ratio tests -- 2.7.1 When to use the asymptotic x2 distribution -- 2.7.2 Testing a subset of real parameters -- 2.7.3 Testing parameters with boundary conditions -- 2.7.4 Testing trees -- 2.8 Concluding remarks -- Acknowledgements -- 3 Bayesian inference in molecular phylogenetics -- 3.1 The likelihood function and maximum likelihood estimates -- 3.2 The Bayesian paradigm -- 3.3 Prior -- 3.4 Markov chain Monte Carlo -- 3.4.1 Metropolis-Hastings algorithm -- 3.4.2 Single-component Metropolis-Hastings algorithm -- 3.4.3 Gibbs sampler -- 3.4.4 Metropolis-coupled MCMC -- 3.5 Simple moves and their proposal ratios -- 3.5.1 Sliding window using uniform proposal -- 3.5.2 Sliding window using normally distributed proposal.; 3.5.3 Sliding window using normal proposal in multidimensions -- 3.5.4 Proportional shrinking and expanding -- 3.6 Monitoring Markov chains and processing output -- 3.6.1 Diagnosing and validating MCMC algorithms -- 3.6.2 Gelman and Rubin's potential scale reduction statistic -- 3.6.3 Processing output -- 3.7 Applications to molecular phylogenetics -- 3.7.1 Estimation of phylogenies -- 3.7.2 Estimation of species divergence times -- 3.8 Conclusions and perspectives -- Acknowledgements -- 4 Statistical approach to tests involving phylogenies -- 4.1 The statistical approach to phylogenetic inference -- 4.2 Hypotheses testing -- 4.2.1 Null and alternative hypotheses -- 4.2.2 Test statistics -- 4.2.3 Significance and power -- 4.2.4 Bayesian hypothesis testing -- 4.2.5 Questions posed as function of the tree parameter -- 4.2.6 Topology of treespace -- 4.2.7 The data -- 4.2.8 Statistical paradigms -- 4.2.9 Distributions on treespace -- 4.3 Different types of tests involving phylogenies.; 4.3.1 Testing t1 versus t2 -- 4.3.2 Conditional tests -- 4.3.3 Modern Bayesian hypothesis testing -- 4.3.4 Bootstrap tests -- 4.4 Non-parametric multivariate hypothesis testing -- 4.4.1 Multivariate con.dence regions -- 4.5 Conclusions: there are many open problems -- Acknowledgements -- 5 Mixture models in phylogenetic inference -- 5.1 Introduction: models of gene-sequence evolution -- 5.2 Mixture models -- 5.3 Defining mixture models -- 5.3.1 Partitioning and mixture models -- 5.3.2 Discrete-gamma model as a mixture model -- 5.3.3 Combining rate and pattern-heterogeneity -- 5.4 Digression: Bayesian phylogenetic inference -- 5.4.1 Bayesian inference of trees via MCMC -- 5.5 A mixture model combining rate and pattern-heterogeneity -- 5.5.1 Selected simulation results -- 5.6 Application of the mixture model to inferring the phylogeny of the mammals -- 5.6.1 Model testing -- 5.7 Results -- 5.7.1 How many rate matrices to include in the mixture model?…”
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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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Cluster analysis

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A Case for the Existence of God. by Overman, Dean L.

Table of Contents: “…Chapter 11: Recorded Experiences of Encounters with the Divine Bear Witness to a Way of Knowing that Includes Kierkegaard's KENDSKAB, BUBER'S I-Thou, OTTO'S Wholly Other, AND MARCEL'S MysteryChapter 12: THESE NINE WITNESSES TESTIFY TO ANOTHER WAY OF KNOWING THAT IS COMPATIBLE WITH THE EMPIRICAL AND THE METAPHYSICAL RATIONAL WAYS OF KNOWING, BUT IS BEYOND THE DESCRIBABLE AND REQUIRES PERSO; Chapter 13: CONCLUDING REFLECTIONS AND SUMMARY; AFTERWORD; Appendix A: THE NEW MATHEMATICS OF ALGORITHMIC INFORMATION THEORY IS RELEVANT TO THEORIES CONCERNING THE FORMATION OF THE FIRST LIVING MATTER.…”
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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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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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Computational phraseology

Table of Contents: “…Monocollocable words : a type of language combinatory periphery / František Čermák -- Translation asymmetries of multiword expressions in machine translation : an analysis of the TED-MWE corpus / Johanna Monti, Mihael Arcan and Federico Sangati -- German constructional phrasemes and their Russian counterparts : a corpus-based study / Dmitrij Dobrovol'skij -- Computational phraseology and translation studies : from theoretical hypotheses to practical tools / Jean-Pierre Colson -- Computational extraction of formulaic sequences from corpora : two case studies of a new extraction algorithm / Alexander Wahl and Stefan Th. Gries -- Computational phraseology discovery in corpora with the MWETOOLKIT / Carlos Ramisch -- Multiword expressions in comparable corpora / Peter Ďurčo -- Collecting collocations from general and specialised corpora : a comparative analysis / Marie-Claude L'Homme and Daphnée Azoulay -- What matters more : the size of the corpora or their quality? …”
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Applications of GRA and grey prediction models

Table of Contents: “…Combined forecasting of regional logistics demand optimized by genetic algorithmApplication of grey relational analysis to expose individual student's cognitive difficulties in English public speaking; Mobile communication service income prediction method based on grey buffer operator theory; Using grey relational analysis to evaluate resource configuration and service ability for hospital on public private partnership model in China; The status of traditional medicine and national medicine in different areas of the China in 2011 with grey clustering analysis.…”
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Neural control engineering : the emerging intersection between control theory and neuroscience by Schiff, Steven J.

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The handbook of news analytics in finance

Table of Contents: “…Kalev and Huu Nhan Duong -- Equity portfolio risk estimation using market information and sentiment / Leela Mitra, Gautam Mitra and Dan diBartolomeo -- Incorporating news into algorithmic trading strategies : increasing the signal-to-noise ratio / Richard Brown -- Are you still trading without news? …”
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Social-behavioral modeling for complex systems

Table of Contents: “…Cover; Title Page; Copyright; Contents; Foreword; List of Contributors; About the Editors; About the Companion Website; Part I Introduction and Agenda; Chapter 1 Understanding and Improving the Human Condition: A Vision of the Future for Social-Behavioral Modeling; Challenges; Challenge One: The Complexity of Human Issues; Challenge Two: Fragmentation; Empirical Observation; Empirical Experiments; Generative Simulation; Unification; Challenge Three: Representations; Challenge Four: Applications of Social-Behavioral Modeling; About This Book; Roadmap for the Book; References…”
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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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Stochastic filtering with applications in finance by Bhar, Ramaprasad

Table of Contents: “…Background to particle filter for non Gaussian problems. 1.8. Particle filter algorithm. 1.9. Unobserved component models. 1.10. …”
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Language learnability and language development by Pinker, Steven, 1954-

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Reviews in computational chemistry.

Table of Contents: “…Small Molecule Docking and Scoring; Introduction; Algorithms for Molecular Docking; The Docking Problem; Placing Fragments and Rigid Molecules; Flexible Ligand Docking; Handling Protein Flexibility; Docking of Combinatorial Libraries; Scoring; Shape and Chemical Complementary Scores; Force Field Scoring; Empirical Scoring Functions; Knowledge-Based Scoring Functions; Comparing Scoring Functions in Docking Experiments: Consensus Scoring.…”
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Performance evaluation and Army recruiting by Dertouzos, James N., 1950-

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Current development of mechanical engineering and energy : selected, peer reviewed papers from the 2013 International Symposium on Vehicle, Mechanical, and Electrical Engineering (...

Table of Contents: “…Simulation and Study on Ride Comfort of Articulated Dump Truck Based on Rigid-Flex CouplingThe Hall Automotive Wheel Speed Sensor and Simulation for its Signal Processing; Wind-Induced Vibration of the Rear-View Mirrors of Car at High Speed; Research on the Affection of Vehicle Lateral Stability Related to the Improved ABS; The Quantification Research of Engine Body Defect that Tested by Ultrasonic Phased Array; Analysis of Vehicle Interior Low-Frequency Noise Based on ATV; The Engine Oil Sump Radiated Noise Optimization Design; A Novel OFDM Radar Algorithm in Vehicular Environment…”
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