Search Results - empirical ((algorithmics OR algorithmus) OR algorithms)

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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Social media analytics for user behavior modeling : a task heterogeneity perspective by Nelakurthi, Arun Reddy, He, Jingrui

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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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Neural networks in chemical reaction dynamics

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Business models : innovation, digital transformation, and analytics

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Indoor Geolocation Science and Technology : At the Emergence of Smart World and IoT. by Pahlavan, Kaveh, 1951-

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Advances In Artificial Intelligence For Privacy Protection And Security.

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Principles of mathematical petrophysics by Doveton, John H., 1944-

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Introduction to OFDM receiver design and simulation by Liu, Y. J.

Table of Contents: “…References-7 Error-Correcting Codes and Interleaver-7.1 Introduction-7.2 Linear Block Codes-7.2.1 Generator Matrix-7.2.2 Parity Check Matrix-7.2.3 Syndrome-7.2.4 Error Correction-7.2.5 Hamming Codes-7.3 Cyclic Codes-7.3.1 Generator Polynomial-7.3.2 Syndrome Polynomial-7.4 Convolutional Code-7.4.1 Convolutional Encoder-7.4.2 Convolutional Decoder and Viterbi Algorithm-7.4.3 Convolutional Code in the IEEE 802.11a-7.4.4 Punctured Convolutional Codes-7.5 Interleaver-7.5.1 Illustration of an Interleaver-7.5.2 Interleaver Used in the IEEE 802.11a…”
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Introduction to Bayesian estimation and copula models of dependence by Shemyakin, Arkady

Table of Contents: “…4 Markov Chain Monte Carlo Methods4.1 Markov Chain Simulations for Sun City and Ten Coins; 4.2 Metropolis-Hastings Algorithm; 4.3 Random Walk MHA; 4.4 Gibbs Sampling; 4.5 Diagnostics of MCMC; 4.5.1 Monitoring Bias and Variance of MCMC; 4.5.2 Burn-in and Skip Intervals; 4.5.3 Diagnostics of MCMC; 4.6 Suppressing Bias and Variance; 4.6.1 Perfect Sampling; 4.6.2 Adaptive MHA; 4.6.3 ABC and Other Methods; 4.7 Time-to-Default Analysis of Mortgage Portfolios; 4.7.1 Mortgage Defaults; 4.7.2 Customer Retention and Infinite Mixture Models; 4.7.3 Latent Classes and Finite Mixture Models.…”
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Reproducibility : principles, problems, practices, and prospects

Table of Contents: “…6.3 Extending BMS (and NML#): BMS* -- 6.4 Replication Variance and Reproducibility -- 6.4.1 Within- and Between-Setting Replication Variance and the True State of the World -- 6.4.2 Reproducibility -- 6.4.3 A Toy Example -- 6.5 Final Remark -- References -- Chapter 7 Reproducibility from the Perspective of Meta-Analysis -- 7.1 Introduction -- 7.2 Basics of Meta-Analysis -- 7.2.1 Conceptual Preliminaries -- 7.2.2 Systematic Reviews -- 7.2.3 Fixed-Effects and Random-Effects Meta-Analysis -- 7.2.4 Biases in Meta-Analysis -- 7.3 Meta-Analysis of Mind-Matter Experiments: A Case Study -- 7.3.1 Statistical Modeling -- 7.3.2 Analysis of the Ramp -- N Data -- 7.4 Summary -- References -- Chapter 8 Why Are There So Many Clustering Algorithms, and How Valid Are Their Results? -- 8.1 Introduction -- 8.1.1 Data Mining and Knowledge Discovery -- 8.1.2 Choices and Assumptions -- 8.2 Supervised and Unsupervised Learning -- 8.3 Cluster Validity as Easiness in Classification -- 8.3.1 Instance Easiness for Supervised Learning -- 8.3.2 Clustering-Quality Measures Based on Supervised Learning -- 8.3.3 Using the Clustering-Quality Measures mp and mc -- 8.4 Applying Clustering-Quality Measures to Data -- 8.4.1 Clustering Based on Prediction Strength -- 8.4.2 Studies with Synthetic Data -- 8.4.3 Studies with Empirical Data -- 8.5 Other Clustering Models -- 8.5.1 Hierarchical Clustering -- 8.5.2 Fuzzy Clustering -- 8.6 Summary -- References -- Part III: Physical Sciences -- Chapter 9 Facilitating Reproducibility in ScientificComputing: Principles and Practice -- 9.1 Introduction -- 9.2 A Culture of Reproducibility -- 9.2.1. …”
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Digital disciplines : attaining market leadership via the cloud, big data, social, mobile, and the internet of things by Weinman, Joe, 1958-

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Evidencing the Productive Value of the 4IR in the AEC Sector. by Ejohwomu, Obuks A.

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Applied Univariate, Bivariate, and Multivariate Statistics Using Python : A Beginner's Guide to Advanced Data Analysis. by Denis, Daniel J.

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

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Handbook of safety principles

Table of Contents: “…Goal Programming / Yan-Fu Li / Enrico Zio -- 23.3.4. Evolutionary Algorithms / Yan-Fu Li / Enrico Zio -- 23.4. Performance Measures / Yan-Fu Li / Enrico Zio -- 23.5. …”
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Modern measurements : fundamentals and applications

Table of Contents: “…PREFACE xv -- ACRONYMS xvii -- I FUNDAMENTALS 1 -- 1 MEASUREMENT MODELS AND UNCERTAINTY 3 / Alessandro Ferrero and Dario Petri -- 1.1 Introduction 3 -- 1.2 Measurement and Metrology 4 -- 1.3 Measurement Along the Centuries 5 -- 1.3.1 Measurement in Ancient Greece 6 -- 1.3.2 Measurement in the Roman Empire 6 -- 1.3.3 Measurement in the Renaissance Period 7 -- 1.3.4 Measurement in the Modern Age 8 -- 1.3.5 Measurement Today 9 -- 1.4 Measurement Model 10 -- 1.4.1 A First Measurement Model 11 -- 1.4.2 A More Complex Measurement Model 16 -- 1.4.3 Final Remarks 19 -- 1.5 Uncertainty in Measurement 20 -- 1.5.1 The Origin of the Doubt 21 -- 1.5.2 The Different Effects on the Measurement Result 23 -- 1.5.3 The Final Effect 25 -- 1.6 Uncertainty Definition and Evaluation 27 -- 1.6.1 The Error Concept and Why it Should be Abandoned 28 -- 1.6.2 Uncertainty Definition: The GUM Approach 29 -- 1.6.3 Evaluating Standard Uncertainty 31 -- 1.6.4 The Combined Standard Uncertainty 35 -- 1.7 Conclusions 39 -- Further Reading 40 -- References 41 -- Exercises 41 -- 2 THE SYSTEM OF UNITS AND THE MEASUREMENT STANDARDS 47 / Franco Cabiati -- 2.1 Introduction 47 -- 2.2 Role of the Unit in the Measurement Process 48 -- 2.3 Ideal Structure of a Unit System 50 -- 2.4 Evolution of the Unit Definition 52 -- 2.5 The SI System of Units 53 -- 2.6 Perspectives of Future SI Evolution 59 -- 2.7 Realization of Units and Primary Standards 62 -- 2.7.1 Meter Realization and Length Standards 65 -- 2.7.2 Kilogram Realization and Mass Standards: Present Situation 66 -- 2.7.3 Kilogram Realization: Future Perspective 67 -- 2.7.4 Realization of the Second and Time Standards 69 -- 2.7.5 Electrical Unit Realizations and Standards: Present Situation 71 -- 2.7.6 Electrical Units Realization and Standards: Future Perspective 76 -- 2.7.7 Kelvin Realization and Temperature Standards: Present Situation 78 -- 2.7.8 Kelvin Realization and Temperature Standards: Future Perspective 79 -- 2.7.9 Mole Realization: Present Situation 80 -- 2.7.10 Mole Realization: Future Perspective 81 -- 2.7.11 Candela Realization and Photometric Standards 82 -- 2.8 Conclusions 83 -- Further Reading 83 -- References 84 -- Exercises 84 -- 3 DIGITAL SIGNAL PROCESSING IN MEASUREMENT 87 / Alessandro Ferrero and Claudio Narduzzi -- 3.1 Introduction 87 -- 3.2 Sampling Theory 88 -- 3.2.1 Sampling and Fourier Analysis 89 -- 3.2.2 Band-Limited Signals 92 -- 3.2.3 Interpolation 95 -- 3.3 Measurement Algorithms for Periodic Signals 96 -- 3.3.1 Sampling Periodic Signals 97 -- 3.3.2 Estimation of the RMS Value 99 -- 3.4 Digital Filters 102 -- 3.5 Measuring Multi-Frequency Signals 106 -- 3.5.1 Finite-Length Sequences 107 -- 3.5.2 Discrete Fourier Transform 111 -- 3.5.3 Uniform Window 113 -- 3.5.4 Spectral Leakage 114 -- 3.5.5 Leakage Reduction by the Use of Windows 116 -- 3.6 Statistical Measurement Algorithms 119 -- 3.7 Conclusions 120 -- Further Reading 121 -- References 122 -- Exercises 122 -- 4 AD AND DA CONVERSION 125 / Niclas BjASorsell -- 4.1 Introduction 125 -- 4.2 Sampling 125 -- 4.2.1 Quantization 126 -- 4.2.2 Sampling Theorem 129 -- 4.2.3 Signal Reconstruction 130 -- 4.2.4 Anti-Alias Filter 133 -- 4.3 Analog-to-Digital Converters 133 -- 4.3.1 Flash ADCs 133 -- 4.3.2 Pipelined ADCs 134 -- 4.3.3 Integrating ADCs 134 -- 4.3.4 Successive Approximation Register ADCs 135 -- 4.4 Critical ADC Parameters 135 -- 4.4.1 Gain and Offset 136 -- 4.4.2 Integral and Differential Non-linearity 137 -- 4.4.3 Total Harmonic Distortion and Spurious-Free Dynamic Range 139 -- 4.4.4 Effective Number of Bits 139 -- 4.5 Sampling Techniques 139 -- 4.5.1 Oversampling 139 -- 4.5.2 Sigma-Delta 140 -- 4.5.3 Dither 141 -- 4.5.4 Time-Interleaved 142 -- 4.5.5 Undersampling 142 -- 4.5.6 Harmonic Sampling 143 -- 4.5.7 Equivalent-Time Sampling 143 -- 4.5.8 Model-Based Post-correction 144 -- 4.6 DAC 144 -- 4.6.1 Binary-Weighted 144 -- 4.6.2 Kelvin Divider 145 -- 4.6.3 Segmented 145 -- 4.6.4 R-2R 145 -- 4.6.5 PWM DAC 145 -- 4.7 Conclusions 146 -- Further Reading 146 -- References 146 -- Exercises 147 -- 5 BASIC INSTRUMENTS: MULTIMETERS 149 / Daniel Slomovitz -- 5.1 Introduction 149 -- 5.2 History 150 -- 5.3 Main Characteristics 153 -- 5.3.1 Ranges 153 -- 5.3.2 Number of Digits and Resolution 155 -- 5.3.3 Accuracy 158 -- 5.3.4 Loading Effects 159 -- 5.3.5 Guard 160 -- 5.3.6 Four Terminals 161 -- 5.3.7 Accessories 162 -- 5.3.8 AC Measurements 164 -- 5.3.9 Safety 167 -- 5.3.10 Calibration 170 -- 5.3.11 Selection 171 -- 5.4 Conclusions 171 -- Further Reading 172 -- References 172 -- Exercises 173 -- 6 BASIC INSTRUMENTS: OSCILLOSCOPES 175 / Jorge Fernandez Daher -- 6.1 Introduction 175 -- 6.2 Types of Waveforms 176 -- 6.2.1 Sinewave 176 -- 6.2.2 Square or Rectangular Wave 176 -- 6.2.3 Triangular or Sawtooth Wave 176 -- 6.2.4 Pulses 177 -- 6.3 Waveform Measurements 177 -- 6.3.1 Amplitude 177 -- 6.3.2 Phase Shift 177 -- 6.3.3 Period and Frequency 177 -- 6.4 Types of Oscilloscopes 177 -- 6.5 Oscilloscope Controls 181 -- 6.5.1 Vertical Controls 183 -- 6.5.2 Horizontal Controls 184 -- 6.5.3 Trigger System 185 -- 6.5.4 Display System 187 -- 6.6 Measurements 188 -- 6.6.1 Peak-to-Peak Voltage 188 -- 6.6.2 RMS Voltage 188 -- 6.6.3 Rise Time 188 -- 6.6.4 Fall Time 188 -- 6.6.5 Pulse Width 188 -- 6.6.6 Period 190 -- 6.6.7 Frequency 190 -- 6.6.8 Phase Shift Measurements 190 -- 6.6.9 Mathematical Functions 190 -- 6.7 Performance Characteristics 191 -- 6.7.1 Bandwidth 191 -- 6.7.2 Rise Time 191 -- 6.7.3 Channels 193 -- 6.7.4 Vertical Resolution 193 -- 6.7.5 Gain Accuracy 193 -- 6.7.6 Horizontal Accuracy 193 -- 6.7.7 Record Length 193 -- 6.7.8 Update Rate 194 -- 6.7.9 Connectivity 195 -- 6.8 Oscilloscope Probes 195 -- 6.8.1 Passive Probes 196 -- 6.8.2 Active Probes 197 -- 6.9 Using the Oscilloscope 199 -- 6.9.1 Grounding 199 -- 6.9.2 Calibration 199 -- 6.10 Conclusions 199 -- Further Reading 200 -- References 200 -- Exercises 201 -- 7 FUNDAMENTALS OF HARD AND SOFT MEASUREMENT 203 / Luca Mari, Paolo Carbone and Dario Petri -- 7.1 Introduction 203 -- 7.2 A Characterization of Measurement 206 -- 7.2.1 Measurement as Value Assignment 206 -- 7.2.2 Measurement as Process Performed by a Metrological System 209 -- 7.2.3 Measurement as Process Conveying Quantitative Information 209 -- 7.2.4 Measurement as Morphic Mapping 210 -- 7.2.5 Measurement as Mapping on a Given Reference Scale 213 -- 7.2.6 Measurement as Process Conveying Objective and Inter-Subjective Information 215 -- 7.2.7 The Operative Structure of Measurement 216 -- 7.2.8 A Possible Definition of?́…”
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The Dictionary of Critical Social Sciences. by Young, T. R.

Table of Contents: “…Cover -- Half Title -- Title -- Copyright -- Dedication -- Preface -- Authors' Note -- Acknowledgments -- A -- AARP -- Abduction -- Abolitionism -- Abortion -- Abuse, Child -- Abuse, Spousal -- Accenting the Sign -- Act -- Act, Philosophy of -- Acton, Lord (1834-1902) -- Actor -- Adolescence -- Adorno, Theodor (1903-1969) -- Adventist Religious Groups -- Advertising -- Aesthetics -- Aesthetics (Socialist) -- Affect -- Age -- Aged, Immiseration of -- Age Grades -- Agency, Human -- Agitation -- Agnosticism -- Agrarian Society -- Agriculture -- Agriculture, Hydraulic Societies -- Agrippa -- Ahistoricism -- Algorithm -- Alienation -- Alienation, Assumptions of -- Alienation, Psychological -- Allegory -- Analogy -- Analysis -- Anarchy -- Androcentricism -- Animal Functions -- Anomia -- Anomie -- Anthropocentrism -- Anthropology -- Anthropomorphism -- Anti-Foundationalism -- Anti-Semitism -- Apathy -- Apollonian -- Apology -- Apple, and Temptation to Knowledge -- Appropriation -- Appropriation, Mechanisms of -- Aquinas, Thomas, St. (1225-1274) -- Archaeology -- Archetype -- Aristocracy -- Aristotle (384-322 B.C.E.) -- Art -- Artificial Intelligence -- Artificial Stupidity -- Asceticism -- Ascribed Status -- Ashby's Law of Requisite Variety -- Assimilation -- Atheism -- Attitude -- Attractor -- Attractor, Strange -- Augustine, St. (354-430) -- Author -- Authoritarianism -- Authoritarian Personality -- Authority -- Automation -- Autonomy -- Away(s) -- Axiology -- Axiom -- B -- Back Stage -- Bacon, Francis (1561-1626) -- Bakunin, Mikhail (1814-1876) -- Balance of Payments -- Balance of Trade -- Banditry, Social -- Barbarism -- Barthes, Roland (1915-1980) -- Base -- Baseball -- Base Communities -- Bastard -- Baudrillard, Jean (1929- ) -- Beauvoir, Simone de (1908-1986) -- Behavior -- Behaviorism -- Behavior Modification -- Belief.…”
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Credit securitizations and derivatives : challenges for the global markets

Table of Contents: “…Market Credit Risk Pricing -- Regulation -- Developments in Structured Finance Markets -- Impairments of Asset-Backed Securities and Outstanding Ratings -- Issuance of Asset-backed Securities and Outstanding Volume -- Global CDO Issuance and Outstanding Volume -- PART II CREDIT PORTFOLIO RISK MEASUREMENT -- Mortgage Credit Risk -- Five C's of Credit and Mortgage Credit Risk -- Determinants of Mortgage Default, Loss Given Default and Exposure at Default -- Determinants of Mortgage Default -- Determinants of Mortgage LGD -- Determinants of Mortgage EAD -- Modeling Methods for Default, LGD and EAD -- Model Risk Management -- Credit Portfolio Correlations and Uncertainty -- Introduction -- Gaussian and Semi-Gaussian Single Risk Factor Model -- Individual and Simultaneous Confidence Bounds and Intervals -- Confidence Intervals for Asset Correlations -- Confidence Intervals for Default and Survival Time Correlations -- Confidence Intervals for Default Correlations -- Confidence Intervals for Survival Time Correlations -- Credit Portfolio Correlations with Dynamic Leverage Ratios -- The Hui et al. (2007) Model -- The Method of Images for Constant Coefficients -- The Method of Images for Time-Varying Coefficients -- Modelling Default Correlations in a Two-Firm Model -- Default Correlations -- A Two-Firm Model with Dynamic Leverage Ratios -- Method of Images for Constant Coefficients -- Method of Images for Time-Varying Coefficients -- Alternative Methodologies for General Values -- Numerical Results -- Accuracy -- The Impact of Correlation between Two Firms -- The Impact of Different Credit Quality Paired Firms -- The Impact of Volatilities -- The Impact of Drift Levels -- The Impact of Initial Value of Leverage Ratio Levels -- Impact of Correlation between Firms and Interest Rates -- The Price of Credit-Linked Notes -- A Hierarchical Model of Tail-Dependent Asset Returns -- The Variance Compound Gamma Model -- Multivariate Process for Logarithmic Asset Returns -- Dependence Structure -- Sampling -- Copula Properties -- An Application Example -- Portfolio Setup -- Test Portfolios -- Parameter Setup -- Simulation Results -- Importance Sampling Algorithm -- Conclusions -- Appendix A: The VCG Probability Distribution Function Appendix B: HAC Representation for the VCG Framework -- Monte Carlo Methods for Portfolio Credit Risk -- Modeling Credit Portfolio Losses -- Risk Measures -- Modeling Dependency -- Estimating Risk Measures via Monte Carlo -- Crude Monte Carlo Estimators -- Importance Sampling -- Specific Models -- The Bernoulli Mixture Model -- Factor Models -- Copula Models -- Intensity Models -- An Example Point Process Model -- Appendix A: A Primer on Rare-event Simulation -- Efficiency -- Importance Sampling -- The Choice of g -- Adaptive Importance Sampling -- Importance Sampling for Stochastic Processes -- Credit Portfolio Risk and Diversification -- Introduction -- Model Setup -- Independent Asset Values -- Correlated Asset Values -- Large Portfolio Limit -- Correlated Diffusion -- Correlated GARCH Process -- Applications of the Structural Recovery Rate -- Conclusions -- PART III CREDIT PORTFOLIO RISK SECURITIZATION AND TRANCHING -- Differences in Tranching Methods: Some Results and Implications -- Defining a Tranche -- The Mathematics of Tranching -- PD-based Tranching -- EL-based Tranching -- The EL of a Tranche Necessarily Increases When Either the Attachment Point or the Detachment Point is Decreased -- Upper Bound on Tranche Expected LGD (LGDt) Assumption Given EL-based Tranches -- Skipping of Some Tranches in the EL-based Approach -- Global Structured Finance Rating -- Asset-Backed Securities -- The ABS Structure for the Experiment -- Cash Flow Modeling -- Modeling and Simulating Defaults -- Expected Loss Rating -- Global Sensitivity Analysis -- Elementary Effects -- Variance-based Method -- Global Sensitivity Analysis Results -- Uncertainty Analysis -- Sensitivity Analysis -- Global Rating -- PART IV CREDIT DERIVATIVES -- Analytic Dynamic Factor Copula Model -- Pricing Equations -- One-factor Copula Model -- Multi-period Factor Copula Models -- Calibration -- Dynamic Modeling of Credit Derivatives -- General Model Choice -- Modeling Option Prices -- Modeling Credit Risk -- Portfolio Credit Derivatives -- Modeling Asset Dynamics -- The Market Model -- The Asset-value Model -- Empirical Analysis -- Elementary Data -- Implied Dividends -- Market Dynamics -- Asset Value Model -- Tranche Pricing -- Out-of-time Application -- Pricing and Calibration in Market Models -- Basic notions -- The model -- Modeling Assumptions -- Absence of Arbitrage -- An affine specification -- Pricing -- Calibration -- Calibration Procedure -- Calibration Results -- Appendix A: Computations -- Counterparty Credit Risk and Clearing of Derivatives -- From the Perspective of an Industrial Corporate with a Focus on Commodity Markets -- Credit exposures in commodity business -- Settlement Exposure -- Performance Exposure -- Example of Fixed Price Deal with Performance Exposure -- Example of a Floating Price Deal with Performance Exposure -- General Remarks on Credit Exposure Concepts -- Ex Ante exposure-reducing techniques -- Payment Terms -- Material Adverse Change Clauses -- Master Agreements -- Netting -- Margining -- Close Out Exposure and Threshold -- Ex Ante risk-reducing techniques -- Credit Enhancements in General -- Parent Company Guarantees -- Letters of Credit -- Credit Insurance -- Clearing via a Central Counterparty -- Ex Post risk-reducing techniques -- Factoring -- Novation -- Risk-reducing Trades -- Hedging with CDS -- Hedging with Contingent-CDS -- Hedging with Puts on Equity -- Ex Post work out considerations -- Practical credit risk management and pricing Peculiarities of commodity markets -- Peculiarities of commodity related credit portfolios -- Credit Risk Capital for a commodity related portfolio measured with an extension of CreditMetrics -- CreditRisk+ study: applied to a commodity related credit portfolio -- CDS Industrial Sector Indices, Credit and Liquidity Risk -- The Data -- Methodology and Results -- Preliminary Analysis -- Common Factor Analysis -- Stability of Relations -- Risk Transfer and Pricing of Illiquid Assets with Loan CDS -- Shipping Market -- Loan Credit Default Swaps -- LCDS Pricing -- Modeling LCDS Under the Intensity-based Model -- Valuation Framework for LCDS -- The Structural Approach -- Credit Risk in Shipping Loans -- Valuation of LCDS on Shipping Loans -- Simulation Model -- Numerical Results -- Appendix A: Monte Carlo Parameterization PART V REGULATION -- Regulatory Capital Requirements for Securitizations -- Regulatory Approaches for Securitizations -- Ratings Based Approach (RBA) -- Supervisory Formula Approach (SFA) -- Standardized Approach (SA) -- Post-crisis Revisions to the Basel Framework -- Regulating OTC Derivatives -- The Wall Street Transparency and Accountability Part of the Dodd-Frank Act of 2010 -- Which Derivatives Will Be Affected? …”
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