Probabilistic Modelling for Advanced Data Analysis

Erscheinungsjahr 2027
ISBN: 978-0-443-45210-9
Verlag: Elsevier Science

Buch, Englisch, 400 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

Probabilistic Modelling for Advanced Data Analysis provides a practical and rigorous guide for data practitioners to effectively implement probabilistic models in real-world scenarios. The book strikes a balance between high-level intuition and technical derivations, offering step-by-step explanations, real-world case studies, and Python implementation examples. The authors offer specific solutions that include modeling and quantifying uncertainty in data-driven decision-making, applying Bayesian inference to real-world problems and implementing scalable probabilistic models for large-scale datasets, all of which contribute to explainable and trustworthy AI.

This book presents readers with theoretical foundations and practical applications of probabilistic modeling, providing a structured approach for researchers, data scientists, and industry professionals. It meets the increasing demand for uncertainty-aware AI models, Bayesian inference, and probabilistic graphical models across various fields of research.

Autoren/Hrsg.

Tyagi, Amit Kumar

Mazumdar, Soumya

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

Part I: Foundations of Probability
1. Introduction to Probabilistic Modeling
2. Basic Probability Concepts
3. Random Variables and Probability Distributions
4. Common Probability Distributions

Part II: Probabilistic Thinking in Science and Engineering
5. Joint, Marginal, and Conditional Probabilities
6. Bayesian Thinking for Scientists and Engineers
7. Markov Chains and Stochastic Processes
8. Monte Carlo Methods and Simulation
9. Parameter Estimation and Maximum Likelihood

Part III: Practical Applications of Probabilistic Models
10. Uncertainty Quantification in Engineering
11. Reliability Engineering and Failure Probabilities
12. Bayesian Inference in Science and Engineering
13. Time Series and Probabilistic Forecasting

Part IV: Advanced Topics and Case Studies
14. Probabilistic Graphical Models
15. Hidden Markov Models (HMMs) and Applications
16. Optimization Under Uncertainty

Part V: Advanced Probability and Statistical Inference
17. Measure Theory and Probability Foundations
18. Information Theory and Entropy
19. Bayesian Inference and Hierarchical Models
19.1. Bayesian conjugate priors
20. Stochastic Differential Equations (SDEs)
21. Extreme Value Theory and Rare Event Modeling

Part VI: Probabilistic Machine Learning and AI
22. Gaussian Processes for Regression and Classification
23. Variational Inference and Approximate Bayesian Computation (ABC)
24. Deep Probabilistic Models and Bayesian Neural Networks
25. Probabilistic Graphical Models (PGMs)
26. Reinforcement Learning with Probabilistic Models

Part VII: Advanced Engineering and Scientific Applications
27. Reliability Theory and Probabilistic Risk Assessment (PRA)
28. Probabilistic Optimization and Decision Theory
29. Uncertainty Quantification in Scientific Computing

Part VIII: Cutting-Edge Topics and Future Directions
30. Random Graphs and Network Science
31. Nonparametric Bayesian Methods
32. Probabilistic Programming and Automated Inference
34. Case Studies in Science and Engineering
35. Conclusion and Future of Probabilistic Modeling in Science and Engineering

Über Autor(innen)

Mazumdar, Soumya
Soumya Mazumdar is an independent researcher with a background in Data Science and Computational Programming from IIT Madras and competence in Computer Science Engineering and Business Systems from Maulana Abul Kalam Azad University of Technology. He is currently pursuing his B.S. in Data Science and applications from IIT Madras. His research interests include Industry 4.0, smart infrastructure, and IoT-enabled early detection systems. In order to address global challenges in technology and sustainability, his current research focuses on developing 6G technology for Society 5.0, utilizing IoT for smart infrastructure and disaster management, integrating AI and machine learning in predictive maintenance, healthcare, and industrial robotics, and investigating the potential of blockchain and big database analytics in tandem.

Tyagi, Amit Kumar
Amit Kumar Tyagi is an Assistant Professor at the National Forensic Sciences University, Gandhinagar, India. He previously held academic and research positions, including at Vellore Institute of Technology (2019–2022), and earned his PhD from Pondicherry Central University in 2018.
He has supervised doctoral and master’s students and contributed to research projects on privacy and security in vehicular applications and medical cyber-physical systems. He has published over 200 peer-reviewed papers and holds more than 25 patents in areas such as deep learning, the Internet of Things, cyber-physical systems, and computer vision.

He has edited over 25 books and authored four books in related fields, and has received multiple research awards. His interests include machine-based communications, blockchain, and secure, privacy-preserving computing. He is a member of ACM and IEEE.