Probabilistic Modelling for Advanced Data Analysis

By Amit Kumar Tyagi PhD.

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.

• Format: Paperback
• Language: English
• ISBN: 0443452105
• ISBN13: 9780443452109
• Release Date: January 2027
• Publisher: Morgan Kaufmann
• Length: 400 Pages
• Weight: 0.99 lbs.