Building Smarter Models by Combining Probabilistic Graphical Models and Neural Networks: Hybrid PGM-Deep Learning Techniques in R

S. Yaya, Olaoluwa

ISBN 13: 9798262342167

Editore: Independently published, 2025

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Dati bibliografici

• Titolo: Building Smarter Models by Combining Probabilistic Graphical Models and Neural Networks: Hybrid PGM-Deep Learning Techniques in R
• Editore: Independently published
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 13: 9798262342167
• Rilegatura: Brossura
• Condizione: As New

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Riassunto

In an era where data drives innovation, the boundaries of artificial intelligence are expanding rapidly. Building Smarter Models by Combining Probabilistic Graphical Models and Neural Networks: Hybrid PGM–Deep Learning Techniques in R introduces readers to a groundbreaking approach that unifies probabilistic reasoning with the representational power of neural networks.
This book is designed for researchers, data scientists, and advanced practitioners who seek to push beyond the limitations of traditional machine learning. Instead of treating probabilistic graphical models (PGMs) and deep learning as separate worlds, it explores how these two paradigms can be harmoniously combined to build smarter, more interpretable, and more robust AI systems.
Starting with the fundamentals of PGMs and neural architectures, the book gradually moves into hybrid modeling strategies where probabilistic inference complements neural representations. Readers are guided through real-world case studies implemented in R, demonstrating how these hybrid models can be used for classification, sequence modeling, causal inference, anomaly detection, reinforcement learning, and more.
The narrative style ensures that even complex mathematical and computational ideas are conveyed with clarity, making it accessible to both academic audiences and industry professionals. Alongside theory, the book provides practical hands-on examples, code snippets, and workflows in R, allowing readers to replicate and extend the techniques for their own projects.
Whether you are exploring Bayesian deep learning, probabilistic programming, or uncertainty-aware neural networks, this book offers a comprehensive roadmap to building AI systems that are not only powerful but also trustworthy. By the final chapters, readers will understand how hybrid PGM–neural network models can form the foundation for the next generation of AI—systems that learn, reason, and generalize with intelligence that mirrors human cognition more closely than ever before.
This is not just a technical manual, but a forward-looking guide for anyone passionate about the future of artificial intelligence.