This book introduces relevant and established data-driven modeling tools currently in use or in development, which will help readers master the art and science of constructing models from data and dive into different application areas. It presents statistical tools useful to individuate regularities, discover patterns and laws in complex datasets, and demonstrates how to apply them to devise models that help to understand these systems and predict their behaviors. By focusing on the estimation of multivariate probabilities, the book shows that the entire domain, from linear regressions to deep learning neural networks, can be formulated in probabilistic terms. This book provides the right balance between accessibility and mathematical rigor for applied data science or operations research students, graduate students in CSE, and machine learning and uncertainty quantification researchers who use statistics in their field. Background in probability theory and undergraduate mathematics is assumed.

‘I really enjoyed reading this book. It offers an expansive tour to the realm of probabilistic data-driven systems modeling, as well as an easily accessible reference for those, such as students, researchers and practitioners, aiming to understand the nature and behaviors of complex systems, as often encountered in the real world.’

Jiming Liu - Hong Kong Baptist University

‘This is a much-needed book that comprehensively reviews data analysis concepts and methods for complex systems.It starts with probability and statistics and clearly and succinctly connects it to information theory and network analysis. I look forward to having it on my shelf, and I will recommend it to all my students!’

J. Doyne Farmer - University of Oxford