Bayesian Inference and Maximum Entropy Methods in Science and Engineering : MaxEnt 37, Jarinu, Brazil, July 09–14, 2017 / Adriano Polpo ... [et al.] editors

• Pubblicazione: Cham, : Springer, 2018
• Titolo uniforme: Bayesian Inference and Maximum Entropy Methods in Science and Engineering
• Descrizione fisica: xvi, 304 p. : ill. ; 24 cm
• Soggetto topico: 60G35 - Signal detection and filtering (aspects of stochastic processes) [MSC 2020]; 62-XX - Statistics [MSC 2020]; 62Axx - Foundational topics in statistics [MSC 2020]; 62Fxx - Parametric inference [MSC 2020]; 65Cxx - Probabilistic methods, stochastic differential equations [MSC 2020]; 81Pxx - Foundations, quantum information and its processing, quantum axioms, and philosophy [MSC 2020]; 82B31 - Stochastic methods applied to problems in equilibrium statistical mechanics [MSC 2020]; 82B41 - Random walks, random surfaces, lattice animals, etc. in equilibrium statistical mechanics [MSC 2020]; 85A35 - Statistical astronomy [MSC 2020]
• Soggetto non controllato: Astrophysics; Biostatistics; Chemistry; Climate Studies; Communications Theory; Comology; Earth Science; Entropy; Fluid mechanics; Genetics; Geophysics; Imprecise Probability; Machine learning; Material Science; Maximum Entropy; Medical Imaging; Non-parametric Mmethods; Robotics; Statistical models; Survival analysis
• Persona (resp. second.): Polpo, Adriano
• Titolo autorizzato: Bayesian Inference and Maximum Entropy Methods in Science and Engineering
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: VAN00124581
• Lo trovi qui: Univ. Vanvitelli
• Localizzazioni e accesso elettronico: http://doi.org/10.1007/978-3-319-91143-4
• Serie: Springer proceedings in mathematics & statistics Berlin [etc.] . -Springer , 2012- ; 239