Advances in State and Parameter Estimation : Theory and Practice (Paperback)

1. Introduction  2. Least Squares and Maximum-Likelihood Methods  3. Kalman Filtering Methods  4. Filtering-cum-Data Fusion with State Delay and Missing Measurements  5. Gaussian-Sum Extended Kalman Filter with Lyapunov Stability Analysis  6. Gaussian-sum Information Filter with Lyapunov Stability Analysis  7. Image Centroid Tracking with Square-Root Kalman Filters  8. Image Centroid Tracking with Fuzzy-Logic-Augmented Filters  9. Image Centroid Tracking-cum-Fusion using New Factorization Filtering Algorithms  10. H-Infinity Fuzzification Filtering and Target Tracking (TT)  11. H-Infinity-Based Observer  12. Deterministic Nonlinear Estimators-Observers and Stability Results  13. Hybrid Global H-Infinity Filter  14. Neural Networks for Parameter Estimation with Lyapunov Stability 15. Interactive Multiple Modelling for Target Tracking with New Algorithms  16. Machine Learning for Estimation  Appendix A  Appendix B  Appendix C

Swarm Intelligence : Theory and Applications in Fog Computing, Beyond 5G Networks, and Information Security (Paperback)

Preface. Resource Management in Fog Computing: Overview and Mathematical Foundation. Swarm Intelligence in IoT and Edge Computing. Optimal Deployment of Multiple IoT Applications on the Fog Computing: A Metaheuristic-Based Approach. Machine Learning and Deep Learning with Swarm Algorithms. Search and Rescue Using UAV SWARM in Disaster Management: A Survey. Trust Based Access Control in IoT Network. Safeguarding User Privacy at the Edge of Fog Computing Networks in Decentralized Distributed Computing. Attack Resilient Authentication Scheme for Fog-centric IoT Device Systems. Navigating Ethical Challenges. A Robust DIW Approach for UAV Image Transmission Using PSMD Encryption. References. Index.?

The Digital Transformation of Product Formulation : Concepts, Challenges, and Applications for Accelerated Innovation (Paperback)

Section 1: Getting Started. 1. The Digital Transformation of R&D Labs. 2. Product Formulation Fundamentals. 3. Defining a Successful Predictive Formulation Project. Section 2: Preparing Your Data. 4. Challenges with Formulation Datasets. 5. Feature Engineering: Enhancing Your Data with Descriptors. 6. Machine Learning for Analysis of Structural Characterization. Section 3: Predictive Modeling. 7. Machine Learning Techniques for Predicting Properties of Formulations. 8. Modeling of Product Formulations Using a Latent Variable Approach. 9. Gaining Trust in Your Model. Section 4: Optimization and Inverse Design. 10. Introduction to Formulation Optimization. 11. Adaptive Experimental Design. 12. Inverse Design via PLS Model Inversion. Section 5: Case Studies and Special Topics. 13. Case Studies. 14. Special Topics. 15. Conclusion.

Statistical Concepts – A Second Course (Hardcover, 2 ed)

1. One-Factor Analysis of Variance ? Fixed-Effects Model? 2. Multiple Comparison Procedures? 3. Factorial Analysis of Variance ? Fixed-Effects Model? 4. Random- and Mixed-Effects Analysis of Variance Models? 5. Hierarchical and Randomized Block Analysis of Variance Models? 6. Simple Linear Regression? 7. Multiple Linear Regression? 8. Introduction to Analysis of Covariance: The One-Factor Fixed-Effects Model with a Single Covariate? 9. Logistic Regression? 10. Mediation and Moderation

Statistical Concepts – A Second Course (Paperback, 2 ed)

1. One-Factor Analysis of Variance ? Fixed-Effects Model? 2. Multiple Comparison Procedures? 3. Factorial Analysis of Variance ? Fixed-Effects Model? 4. Random- and Mixed-Effects Analysis of Variance Models? 5. Hierarchical and Randomized Block Analysis of Variance Models? 6. Simple Linear Regression? 7. Multiple Linear Regression? 8. Introduction to Analysis of Covariance: The One-Factor Fixed-Effects Model with a Single Covariate? 9. Logistic Regression? 10. Mediation and Moderation

Statistical Concepts – A First Course (Hardcover, 2 ed)

1. Introduction??2. Data Visualization??3. Univariate Population Parameters and Sample Statistics??4. The Normal Distribution and Standard Scores??5. Introduction to Probability and Sample Statistics??6. Introduction to Hypothesis Testing: Inferences About a Single Mean??7. Inferences About the Difference Between Two Independent Means??8. Inferences About the Difference Between Two Dependent Means??9. Inferences About Proportions??10. Inferences About Variances??11. Bivariate Measures of Association

Statistical Concepts – A First Course (Paperback, 2 ed)

1. Introduction??2. Data Visualization??3. Univariate Population Parameters and Sample Statistics??4. The Normal Distribution and Standard Scores??5. Introduction to Probability and Sample Statistics??6. Introduction to Hypothesis Testing: Inferences About a Single Mean??7. Inferences About the Difference Between Two Independent Means??8. Inferences About the Difference Between Two Dependent Means??9. Inferences About Proportions??10. Inferences About Variances??11. Bivariate Measures of Association

A Guide to Doing Statistics in Second Language Research Using SPSS, JASP, and R (Hardcover, 3 ed)

Part I: Statistical Ideas? 1. Getting Started with the Software? 2. Some Preliminaries to Understanding Statistics? 3. Describing Data Numerically and Graphically and Assessing Assumptions for Parametric Tests? 4.?The "Old" Statistics: Fundamentals of Null-Hypothesis Significance Testing? 5. The "New" Statistics: Confidence Intervals, Effect Sizes, and precision for Sample Size Determination?6. Using AI Tools to Learn About Statistics??Part II: Statistical Tests? ?7.?Finding Relationships Using Correlation: Age of Learning? 8. Looking for Groups of Explanatory Variables through Multiple Regression: Predicting Important Factors in First-Grade Reading? 9. Looking for Differences between Two Means with T-Tests: Think-Aloud Methodology and Teaching Sarcasm? 10. Looking for Group Differences with a One-Way Analysis of Variance: Effects of Planning Time on Essay Writing??11. Looking for Group Differences with Factorial Analysis of Variance When There is More than One Independent Variable: Learning with Music and Pictures??12. Looking for Group Differences when the Same People Are Tested More than Once: Repeated-Measures Analysis of Variance with Wug Tests and Instruction on French Gender? 13. Accoutning for Differences between People and Items with Linear Mixed-Effects Models

A Guide to Doing Statistics in Second Language Research Using SPSS, JASP, and R (Paperback, 3 ed)

Part I: Statistical Ideas? 1. Getting Started with the Software? 2. Some Preliminaries to Understanding Statistics? 3. Describing Data Numerically and Graphically and Assessing Assumptions for Parametric Tests? 4.?The "Old" Statistics: Fundamentals of Null-Hypothesis Significance Testing? 5. The "New" Statistics: Confidence Intervals, Effect Sizes, and precision for Sample Size Determination?6. Using AI Tools to Learn About Statistics??Part II: Statistical Tests? ?7.?Finding Relationships Using Correlation: Age of Learning? 8. Looking for Groups of Explanatory Variables through Multiple Regression: Predicting Important Factors in First-Grade Reading? 9. Looking for Differences between Two Means with T-Tests: Think-Aloud Methodology and Teaching Sarcasm? 10. Looking for Group Differences with a One-Way Analysis of Variance: Effects of Planning Time on Essay Writing??11. Looking for Group Differences with Factorial Analysis of Variance When There is More than One Independent Variable: Learning with Music and Pictures??12. Looking for Group Differences when the Same People Are Tested More than Once: Repeated-Measures Analysis of Variance with Wug Tests and Instruction on French Gender? 13. Accoutning for Differences between People and Items with Linear Mixed-Effects Models

An Introduction to Statistical Concepts (Hardcover, 5 ed)

1. Introduction? 2. Data Visualization??3. Univariate Population Parameters and Sample Statistics??4. The Normal Distribution and Standard Scores??5. Introduction to Probability and Sample Statistics? 6. Introduction to Hypothesis Testing: Inferences About a Single Mean??7. Inferences About the Difference Between Two Independent Means? 8. Inferences About the Difference Between Two Dependent Means??9. Inferences About Proportions? 10. Inferences About Variances? 11. Bivariate Measures of Association??12. One-Factor Analysis of Variance ? Fixed-Effects Model??13. Multiple Comparison Procedures??14. Factorial Analysis of Variance ? Fixed-Effects Model??15. Random- and Mixed-Effects Analysis of Variance Models??16. Hierarchical and Randomized Block Analysis of Variance Models??17. Simple Linear Regression??18. Multiple Linear Regression??19. Introduction to Analysis of Covariance: The One-Factor Fixed-Effects Model with a Single Covariate??20. Logistic Regression??21. Mediation and Moderation

Urban Knowledge Graph: Construction, Representation and Application (Hardcover)

Non-Linearity in Financial Modeling: Practical Guide to Machine Learning in Financial Applications (Hardcover)

Consistency-Aware System Design and Verification (Hardcover)

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2025 (Paperback)

Spatial Data Mining: Theory and Application (Hardcover, 2, Second 2026)

Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy: Proceedings of the Fifth International Conference, Mmcitre 2025 (Paperback)

Foundations of Intelligent Systems: 28th International Symposium, Ismis 2026, Lyon, France, July 22-24, 2026, Proceedings (Paperback)

How to Use SPSS® : A Step-By-Step Guide to Analysis and Interpretation (Hardcover, 13 ed)

Preface to the Thirteenth Edition. 1. Getting Started. 2. Entering and Modifying Data. 3. Descriptive Statistics. 4. Graphing Data. 5. Prediction and Association. 6. Basic Parametric Inferential Statistics and t Tests. 7. ANOVA Models. 8. Nonparametric Inferential Statistics. 9. Test Construction. Appendix A. Effect Size. Appendix B. Practice Exercise Datasets. Appendix C. Sample Data Files Used in Text. Appendix D. SPSS Syntax Basics. Appendix E. Glossary. Appendix F. Selecting the Appropriate Inferential Test. Appendix G. Answer Key

How to Use SPSS® : A Step-By-Step Guide to Analysis and Interpretation (Paperback, 13 ed)

Preface to the Thirteenth Edition. 1. Getting Started. 2. Entering and Modifying Data. 3. Descriptive Statistics. 4. Graphing Data. 5. Prediction and Association. 6. Basic Parametric Inferential Statistics and t Tests. 7. ANOVA Models. 8. Nonparametric Inferential Statistics. 9. Test Construction. Appendix A. Effect Size. Appendix B. Practice Exercise Datasets. Appendix C. Sample Data Files Used in Text. Appendix D. SPSS Syntax Basics. Appendix E. Glossary. Appendix F. Selecting the Appropriate Inferential Test. Appendix G. Answer Key

Spatial Crowdsourcing (Hardcover)

High-Dimensional Imaging Data Analysis: From Classical Pca to Tensor Robust Pca with Ai/ML, Vol. 1 (Hardcover)

Rescaled SIR Epidemic Processes Converge to Super–Brownian Motion in Four or More Dimensions (Paperback)

Transient, Steady–State, and Ruin Probabilities of Markov and Gambling Models (Paperback)

de Branges' Theory for Processes with Stationary Increments (Hardcover)