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.
- 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
