The book presents a general mathematical framework able to detect and to characterize, from a morphological and statistical perspective, patterns hidden in spatial data. The mathematical tool employed is a Gibbs point process with interaction, which permits us to reduce the complexity of the pattern. It presents the framework, step by step, in three major parts: modeling, simulation, and inference. Each of these parts contains a theoretical development followed by applications and examples.

Features:

• Presents mathematical foundations for tackling pattern detection and characterisation in spatial data using marked Gibbs point processes with interactions
• Proposes a general methodology for morphological and statistical characterisation of patterns based on three branches, probabilistic modeling, stochastic simulation, and statistical inference
• Includes application examples from cosmology, environmental sciences, geology, and social networks
• Presents theoretical and practical details for the presented algorithms in order to be correctly and efficiently used
• Provides access to C++ and R code to encourage the reader to experiment and to develop new ideas
• Includes references and pointers to mathematical and applied literature to encourage further study

The book is primarily aimed at researchers in mathematics, statistics, and the above-mentioned application domains. It is accessible for advanced undergraduate and graduate students, so could be used to teach a course. It will be of interest to any scientific researcher interested in formulating a mathematical answer to the always challenging question: what is the pattern hidden in the data?

About the Author

Radu S. Stoica is a full professor in mathematics at 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, that are able to characterize and detect structures and patterns that are either hidden or directly observed in the data. The tackled application domains are: astronomy, geosciences, image analysis and network sciences. Previously to his current position, Radu Stoica was associate professor at University of Lille (France). He was also worked as a researcher for INRAe Avignon (France), University Jaume I (Spain) and CWI Amsterdam (The Netherlands).

Book Information
ISBN 9781032459363
Author Radu Stoica
Format Hardback
Page Count 296
Imprint Chapman & Hall/CRC
Publisher Taylor & Francis Ltd
Weight(grams) 453g