Applications of Discrete-time Markov Chains and Poisson Processes to Air Pollution Modeling and Studies

DOI10.1007/978-1-4614-4645-3zbMath1275.62078OpenAlexW606630473MaRDI QIDQ2897220

Eliane R. Rodrigues, Jorge Alberto Achcar

Publication date: 10 July 2012

Published in: SpringerBriefs in Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-1-4614-4645-3

zbMATH Keywords

counting processesBayesian approach

Mathematics Subject Classification ID

Software, source code, etc. for problems pertaining to statistics (62-04) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)

Related Items (3)

Mining periodic patterns and cascading bursts phenomenon in individual e-mail communication ⋮ An application of a non-homogeneous Poisson model to study PM_2.5 exceedances in Mexico City and Bogota ⋮ Genetic algorithm with a Bayesian approach for multiple change-point detection in time series of counting exceedances for specific thresholds

Uses Software

• R
• MHadaptive

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