Probabilistic Modelling for Advanced Data Analysis

Häftad, Engelska, 2027

2 249 kr

Kommande

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 modelling 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. Probabilistic modeling is a crucial tool in data analysis due to big data, artificial intelligence, and complex decision-making. Traditional statistical methods often fail to capture the inherent uncertainty in real-world datasets. This book presents readers with theoretical foundations and practical applications of probabilistic modeling, providing a structured approach for researchers, data scientists, and industry professionals. The book meets the increasing demand for uncertainty-aware AI models, Bayesian inference, and probabilistic graphical models across various fields of research. The authors have written a comprehensive handbook for probabilistic modelling, incorporating diverse perspectives and real-world case studies from a variety of fields. The book is written with accessibility in mind, benefiting readers from various backgrounds, including those new to the field.

Includes real-world case studies from various industries and step-by-step Python implementations of probabilistic models
Presents visual explanations, graphical representations, easy-to-follow analogies, and a focus on Bayesian methods, uncertainty quantification, and probabilistic inference
Features approximate inference techniques, probabilistic deep learning approaches for AI applications, and strategies for handling high-dimensional data with probabilistic models

Produktinformation

Utgivningsdatum2027-01-01
Mått191 x 235 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor400
FörlagElsevier Science
ISBN9780443452109

Tillhör följande kategorier

Tillämpad matematik inom Naturvetenskap och teknik
Databaser inom Data och IT
Artificiell intelligens inom Data och IT
Systemvetenskap och AI inom Data och IT

Amit Kumar Tyagi is an Assistant Professor, at the National Forensic Sciences University, Gandhinagar, Gujarat, India. Previously he worked as an Assistant Professor (Senior Grade 2), and Senior Researcher at Vellore Institute of Technology (VIT), Chennai Campus, India from 2019-2022. He received his Ph.D. Degree (Full-Time) in 2018 from Pondicherry Central University, India. He joined the Lord Krishna College of Engineering, Ghaziabad (LKCE) from 2009 to 2010, and 2012 to 2013. He was an Assistant Professor and head researcher at Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), India from 2018 to 2019. He supervised one PhD thesis and more than ten Master dissertations. He has contributed to several projects such as “AARIN and “P3- Block to address some of the open issues related to privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber-Physical Systems (MCPS). He has published over 200 papers in refereed high-impact journals, conferences, and books, and some of his articles won best paper awards. Also, he has filed more than 25 patents (Nationally and Internationally) in the areas of Deep Learning, Internet of Things, Cyber-Physical Systems, and Computer Vision. He has edited more than 25 books for IET, Elsevier, Springer, CRC Press, etc. Additionally, he has authored 4 Books on Intelligent Transportation Systems, Vehicular Ad-hoc Network, Machine learning and Internet of Things, with IET UK, Springer Germany, and BPB India publisher. He won the Faculty Research Award of the Year for 2020, 2021, and 2022 consecutively, given by Vellore Institute of Technology, Chennai, India. Recently, he was awarded the best paper award for his paper “A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient, in ICCSA 2020, Italy (Europe). His current research focuses on Next Generation Machine Based Communications, Blockchain Technology, Smart and Secure Computing and Privacy. He is a regular member of the ACM, IEEE, MIRLabs, Ramanujan Mathematical Society, Cryptology Research Society, Universal Scientific Education and Research Network, CSI, and ISTE. 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.

Part I: Foundations of Probability1. Introduction to Probabilistic Modeling2. Basic Probability Concepts3. Random Variables and Probability Distributions4. Common Probability DistributionsPart II: Probabilistic Thinking in Science and Engineering5. Joint, Marginal, and Conditional Probabilities6. Bayesian Thinking for Scientists and Engineers7. Markov Chains and Stochastic Processes8. Monte Carlo Methods and Simulation9. Parameter Estimation and Maximum LikelihoodPart III: Practical Applications of Probabilistic Models10. Uncertainty Quantification in Engineering11. Reliability Engineering and Failure Probabilities12. Bayesian Inference in Science and Engineering13. Time Series and Probabilistic ForecastingPart IV: Advanced Topics and Case Studies14. Probabilistic Graphical Models15. Hidden Markov Models (HMMs) and Applications16. Optimization Under UncertaintyPart V: Advanced Probability and Statistical Inference17. Measure Theory and Probability Foundations18. Information Theory and Entropy19. Bayesian Inference and Hierarchical Models19.1. Bayesian conjugate priors20. Stochastic Differential Equations (SDEs)21. Extreme Value Theory and Rare Event ModelingPart VI: Probabilistic Machine Learning and AI22. Gaussian Processes for Regression and Classification23. Variational Inference and Approximate Bayesian Computation (ABC)24. Deep Probabilistic Models and Bayesian Neural Networks25. Probabilistic Graphical Models (PGMs)26. Reinforcement Learning with Probabilistic ModelsPart VII: Advanced Engineering and Scientific Applications27. Reliability Theory and Probabilistic Risk Assessment (PRA)28. Probabilistic Optimization and Decision Theory29. Uncertainty Quantification in Scientific ComputingPart VIII: Cutting-Edge Topics and Future Directions30. Random Graphs and Network Science31. Nonparametric Bayesian Methods32. Probabilistic Programming and Automated Inference34. Case Studies in Science and Engineering35. Conclusion and Future of Probabilistic Modeling in Science and Engineering