Search Results - empirical ((((algoritmica OR algorithm) OR algorithmics) OR logarithmically) OR algorithms)

Regression methods for medical research by Tai, Bee Choo

Table of Contents: “…Regression Methods for Medical Research; Copyright; Contents; Preface; 1 Introduction; Introduction; Statistical models; Comparing two means; Linear regression; Types of dependent variables (y-variables); Some completed studies; Further reading; Technical details; Student's t-test; Linear regression; Predicting a mean value of y for a particular x; Predicting an individual's value of y for a particular x; Analysis of Variance (ANOVA); Coefficient of determination; Extending the simple linear model; Correlation; Logarithms and the exponential constant, e.…”
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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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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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Geophysical Data Analysis : MATLAB Edition. by Menke, William

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Informatics for Health by Randell, R.

Table of Contents: “…An Automatic Approach for Analyzing Treatment Effectiveness Based on Medication Hierarchy -- The Myocardial Infarction Case Study -- Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data -- Dermatology Disease Prediction Based on Two Step Cascade Genetic Algorithm Optimization of ANFIS Parameters -- Querying EHRs with a Semantic and Entity-Oriented Query Language -- Evaluation of the Terminology Coverage in the French Corpus LiSSa -- Linked Data Applications Through Ontology Based Data Access in Clinical Research -- Epidemiological Models Lacking Process Noise Can Be Overconfident -- Disentangling Prognostic and Predictive Biomarkers Through Mutual Information -- IntegrIT -- Towards Utilizing the Swedish National Health Information Exchange Platform for Clinical Research -- Introducing a Method for Transformation of Paper-Based Research Data into Concept-Based Representation with openEHR -- The P̀EARL' Data Warehouse: Initial Challenges Faced with Semantic and Syntactic Interoperability -- Combining Different Privacy-Preserving Record Linkage Methods for Hospital Admission Data -- Application of Correspondence Analysis to Graphically Investigate Associations Between Foods and Eating Locations -- Data Driven Quality Improvement of Health Professions Education: Design and Development of CLUE -- An Interactive Curriculum Data Visualization Tool -- Developing Healthcare Data Analytics APPs with Open Data Science Tools -- Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data -- Development and Evaluation of a Case-Based Retrieval Service -- Learning Differentially Expressed Gene Pairs in Microarray Data -- Developing a Manually Annotated Corpus of Clinical Letters for Breast Cancer Patients on Routine Follow-Up -- Automated Diagnosis Coding with Combined Text Representations.…”
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Managerial Economics : a Mathematical Approach. by Alhabeeb, M. J.

Table of Contents: “…Cover; Title Page; Copyright; Epigraph; Dedication; Preface; Unit I: Methodological Preliminaries; Chapter 1: Qualitative Fundamentals; 1.1 ECONOMIC THEORY AND MANAGERIAL ECONOMICS; 1.2 SOME METHODOLOGICAL FALLACIES; 1.3 PARADIGMS, MODELS, AND THE SCIENTIFIC METHOD; 1.4 THE DESCRIPTIVE AND PRESCRIPTIVE TREATMENTS; 1.5 THE PROFIT FUNCTION: ACCOUNTING VERSUS ECONOMICS; 1.6 ENTREPRENEURSHIP, MANAGEMENT, AND LEADERSHIP; SUMMARY; KEY TERMS; EXERCISES; Chapter 2: Quantitative Fundamentals; 2.1 INTRODUCTION; 2.2 FUNCTIONS; 2.3 EXPONENTS; 2.4 LOGARITHMS AND THE NUMBER e; 2.5 DIFFERENTIAL CALCULUS.…”
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Mathematical methods in defense analyses by Przemieniecki, J. S.

Table of Contents: “…Characteristic Properties of Weapons -- 2.1 Weapon Performance Data -- 2.2 Empirical Averages -- Arithmetic Mean (Average) -- Median.…”
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Humanizing the Education Machine : How to Create Schools That Turn Disengaged Kids Into Inspired Learners. by Miller, Rex

Table of Contents: “…; Digital Immigrants and Natives; The Gutenberg Revolution; The Google Revolution; Disrupting a Linear World with Logarithmic Change; Chapter 7: The Learning Matrix: Mapping the Five Eras of Learning; Toppling Old Empires; The Pattern of Technological Change; Why Would I Send My Kid to Your School?…”
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Statistics and the Evaluation of Evidence for Forensic Scientists. by Aitken, Colin

Table of Contents: “…2.2.2 Examples -- 2.3 The Odds Form of Bayes' Theorem -- 2.3.1 Likelihood Ratio -- 2.3.2 Bayes' Factor and Likelihood Ratio -- 2.3.3 Three-Way Tables -- 2.3.4 Logarithm of the Likelihood Ratio -- 2.4 The Value of Evidence -- 2.4.1 Evaluation of Forensic Evidence -- 2.4.2 Justification of the Use of the Likelihood Ratio -- 2.4.3 Single Value for the Likelihood Ratio -- 2.4.4 Role of Background Information -- 2.4.5 Summary of Competing Propositions -- 2.4.6 Qualitative Scale for the Value of the Evidence -- 2.5 Errors in Interpretation -- 2.5.1 Fallacy of the Transposed Conditional…”
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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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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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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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Statistical analysis in forensic science : evidential value of multivariate physicochemical data

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Statistical Disclosure Control. by Hundepool, Anco

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Rationality and decision making : from normative rules to heuristics

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Sigma-Delta Converters. by De la Rosa, José M.

Table of Contents: “…6.3.1 Hardware Emulation of CT-Ms on an FPGA 257 -- 6.3.2 GPU-accelerated Computing of CT-Ms 258 -- 6.4 Using Multi-objective Evolutionary Algorithms to Optimize Ms 259 -- 6.4.1 Combining MOEA with SIMSIDES 261 -- 6.4.2 Applying MOEA and SIMSIDES to the Synthesis of CT-Ms 262 -- 6.5 Summary 269 -- References 269 -- 7 Electrical Design of ??…”
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Analyzing neural time series data : theory and practice by Cohen, Mike X., 1979-

Table of Contents: “…What Is the Surface Laplacian? -- 22.2. Algorithms for Computing the Surface Laplacian for EEG Data -- 22.3. …”
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Modern portfolio theory : foundations, analysis, and new developments + website by Francis, Jack Clark

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