LEADER 04374nam 2200661 a 450 001 9910437871903321 005 20200520144314.0 010 $a1-283-62439-7 010 $a9786613936844 010 $a1-4614-4645-7 024 7 $a10.1007/978-1-4614-4645-3 035 $a(CKB)2670000000246064 035 $a(EBL)994116 035 $a(OCoLC)809852941 035 $a(SSID)ssj0000736353 035 $a(PQKBManifestationID)11489626 035 $a(PQKBTitleCode)TC0000736353 035 $a(PQKBWorkID)10771547 035 $a(PQKB)10806077 035 $a(DE-He213)978-1-4614-4645-3 035 $a(MiAaPQ)EBC994116 035 $a(PPN)168300834 035 $a(EXLCZ)992670000000246064 100 $a20120719d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplications of discrete-time Markov chains and poisson processes to air pollution modeling and studies /$fEliane Regina Rodrigues, Jorge Alberto Achcar 205 $a1st ed. 2013. 210 $aNew York $cSpringer$d2013 215 $a1 online resource (115 p.) 225 0$aSpringerBriefs in mathematics,$x2191-8198 300 $aDescription based upon print version of record. 311 $a1-4614-4644-9 320 $aIncludes bibliographical references and index. 327 $aApplications of Discrete-time Markov Chains and Poisson Processes to Air Pollution Modeling and Studies; Acknowledgements; Contents; Chapter1 Introduction; Chapter2 Markov Chain Models; 2.1 Introduction; 2.2 Description of the Mathematical Model; 2.3 Bayesian Formulation; 2.4 Application to Ozone Air Pollution; Chapter3 Poisson Models and Their Application to Ozone Data; 3.1 Introduction; 3.2 Homogeneous Poisson Models; 3.3 Non-homogeneous Poisson Models; 3.4 Models with the Presence of Change-Points; Chapter4 Modeling the Time Between Ozone Exceedances; 4.1 Introduction 327 $a4.2 The Mathematical Models4.3 An Application to Ozone Data; Chapter5 Some Counting Processes and Ozone Air Pollution; 5.1 Introduction; 5.2 Description of the Independent and Bivariate Models; 5.3 A Copula Model; Chapter6 Comments; References; Appendix: Program Code; A.1 R Code for the Non-homogeneous Poisson Models with No Change-Points; A.1.1 Weibull Rate Function; A.1.2 Generalized Goel-Okumoto Rate Function; A.1.3 Musa-Okumoto Rate Function; A.2 WinBugs Code; A.2.1 WinBugs Code for the Non-homogeneous Models with One Change-Point; A.2.2 WinBugs Code for the Times Between Exceedances 327 $aA.2.2.1 Model IA.2.2.2 Model II; A.2.2.3 Model III; A.2.2.4 Model IV; A.2.2.5 Multiple Change-Points; Index 330 $aIn this brief we consider some stochastic models that may be used to study problems related to environmental matters, in particular, air pollution.  The impact of exposure to air pollutants on people's health is a very clear and well documented subject. Therefore, it is very important to obtain ways to predict or explain the behaviour of pollutants in general. Depending on the type of question that one is interested in answering, there are several of ways studying that problem. Among them we may quote, analysis of the time series of the pollutants' measurements, analysis of the information obtained directly from the data, for instance, daily, weekly or monthly averages and standard deviations. Another way to study the behaviour of pollutants in general is through mathematical models. In the mathematical framework we may have for instance deterministic or stochastic models. The type of models that we are going to consider in this brief are the stochastic ones. 410 0$aSpringerBriefs in Mathematics,$x2191-8198 606 $aMarkov processes 606 $aAir$xPollution$xComputer simulation 606 $aAir$xPollution$xStudy and teaching 615 0$aMarkov processes. 615 0$aAir$xPollution$xComputer simulation. 615 0$aAir$xPollution$xStudy and teaching. 676 $a628.532 700 $aRodrigues$b Regina Eliane$01762518 701 $aAchcar$b Jorge Alberto$01762519 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437871903321 996 $aApplications of discrete-time Markov chains and poisson processes to air pollution modeling and studies$94202500 997 $aUNINA