I Introduction: what is this book about
1. Introduction
II Define the pattern: probabilistic modelling
2. Marked point processes
3. Applications
III Build the pattern: Markov chains Monte Carlo simulation
4. Markov chains: notions, properties and simulation algorithms
5. Applications
IV Describe the pattern: statistical inference
6. Mathematical tools for statistical pattern detection and characterisation
7. Applications
Biography
Radu S. Stoica is a full professor in mathematics at the University of Lorraine, France. His research activity connects stochastic geometry, spatial statistics, and Bayesian inference for probabilistic modeling and statistical description of random structures and patterns. The results of his work consist of tailored to the data methodologies based on Gibbs Markov models, Monte Carlo algorithms, and inference procedures, which can characterise and detect structures and patterns either hidden or directly observed in the data. The tackled application domains are astronomy, geosciences, image analysis, and network sciences. Prior to his current position, Dr. Stoica was an associate professor at University of Lille, France. He also worked as a researcher for INRAe Avignon, France, University Jaume I, Spain, and CWI Amsterdam, The Netherlands.
