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Book synopsis

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

Book details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Probabilistic Modelling for Advanced Data Analysis
• Author: Amit Kumar Tyagi, Soumya Mazumdar
• Language: English
• Binding: Paperback
• Number of pages: 400
• EAN: 9780443452109
• ISBN: 0443452105
• ID: 52500551
• Publisher: Elsevier Science
• Weight: 450 g
• Dimensions: 235 × 191 mm
• Date of publishing: 01. January 2027

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