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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: Chiron Media, Wallingford, Vereinigtes Königreich
hardcover. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Artikel-Nr. 6666-TNFPD-9781032459363
Anzahl: 5 verfügbar
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: New. Artikel-Nr. 48410404-n
Anzahl: 10 verfügbar
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: As New. Unread book in perfect condition. Artikel-Nr. 48410404
Anzahl: 10 verfügbar
Anbieter: California Books, Miami, FL, USA
Zustand: New. Artikel-Nr. I-9781032459363
Anzahl: Mehr als 20 verfügbar
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
Hardback. Zustand: New. The book presents a general mathematical framework able to detect and to characterise, from a morphological and statistical perspective, patterns hidden in spatial data. The mathematical tools employed are Gibbs Markov processes, mainly marked point procesess 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.FeaturesPresents mathematical foundations for tackling pattern detection and characterisation in spatial data using marked Gibbs point processes with interactionsIncludes application examples from cosmology, environmental sciences, geology, and social networksPresents theoretical and practical details for the presented algorithms in order to be correctly and efficiently usedProvides access to C++ and R code to encourage the reader to experiment and to develop new ideasIncludes references and pointers to mathematical and applied literature to encourage further studyRandom Patterns and Structures in Spatial Data is primarily aimed at researchers in mathematics, statistics, and the above-mentioned application domains. It is accessible for advanced undergraduate and graduate students and thus 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? Artikel-Nr. LU-9781032459363
Anzahl: Mehr als 20 verfügbar
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Hardcover. Zustand: Brand New. 296 pages. 10.00x7.00x9.21 inches. In Stock. Artikel-Nr. x-1032459360
Anzahl: 2 verfügbar
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
Hardback. Zustand: New. The book presents a general mathematical framework able to detect and to characterise, from a morphological and statistical perspective, patterns hidden in spatial data. The mathematical tools employed are Gibbs Markov processes, mainly marked point procesess 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.FeaturesPresents mathematical foundations for tackling pattern detection and characterisation in spatial data using marked Gibbs point processes with interactionsIncludes application examples from cosmology, environmental sciences, geology, and social networksPresents theoretical and practical details for the presented algorithms in order to be correctly and efficiently usedProvides access to C++ and R code to encourage the reader to experiment and to develop new ideasIncludes references and pointers to mathematical and applied literature to encourage further studyRandom Patterns and Structures in Spatial Data is primarily aimed at researchers in mathematics, statistics, and the above-mentioned application domains. It is accessible for advanced undergraduate and graduate students and thus 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? Artikel-Nr. LU-9781032459363
Anzahl: Mehr als 20 verfügbar