LEADER 02757nam 2200565 450 001 9910554873303321 005 20211120144349.0 010 $a1-119-68696-2 010 $a1-119-68694-6 010 $a1-119-68695-4 035 $a(CKB)4940000000599407 035 $a(MiAaPQ)EBC6561801 035 $a(Au-PeEL)EBL6561801 035 $a(OCoLC)1247659940 035 $a(JP-MeL)3000131726 035 $a(EXLCZ)994940000000599407 100 $a20211120d2021 uy 0 101 0 $aeng 135 $aurcn#|||a|a|| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLean architecture $eexcellence in project delivery /$fMichael F. Czap, Gregory T. Buchanan 210 1$aHoboken, New Jersey :$cWiley,$d[2021] 210 4$d2021 215 $a1 online resource (349 pages) $cillustrations 300 $aIncludes index. 311 08$aPrint version: Czap, Michael F. Lean architecture Hoboken, New Jersey : Wiley, [2021] 9781119686934 (DLC) 2020055853 327 $aA profession ripe for change -- Process management explained -- The design firm's problem -- Lean architecture -- Learning from Detroit : influences from lean manufacturing -- Lean management for architects -- Stragetic areas -- Streamlining documentation -- Rethinking your firm -- Role of technology -- More than an initiative. 330 $a"An architectural methodology based around Lean principles, this book challenges architects to examine how they practice by better understanding their goals and by viewing and producing their work differently. Lean Architecture: Excellence in Project Delivery is a lean implementation guide for design firm leadership, project managers, and architecture professionals. This guide to the building blocks of Lean for design firms outlines important metrics and time-tested implementation strategies for firms of all sizes, presents relatable examples highlighting successful Lean application and describes how to avoid common obstacles in introducing and integrating Lean practice across a design firm"--$cProvided by publisher. 606 $aOrganizational effectiveness 606 $aArchitectural practice$xManagement 606 $aLean management 615 0$aOrganizational effectiveness. 615 0$aArchitectural practice$xManagement. 615 0$aLean management. 676 $a720.68 686 $a525.1$2njb/09 686 $a524$2njb/09 686 $a724/.7$2njb/09 686 $a724.68$2njb/09 700 $aCzap$b Michael F.$01383768 702 $aBuchanan$b Gregory T. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910554873303321 996 $aLean architecture$93429071 997 $aUNINA LEADER 04040nam 22005775 450 001 9910255456703321 005 20250315152907.0 010 $a9783319710303 010 $a3319710303 024 7 $a10.1007/978-3-319-71030-3 035 $a(CKB)3790000000544837 035 $a(DE-He213)978-3-319-71030-3 035 $a(MiAaPQ)EBC5215418 035 $a(PPN)223956619 035 $a(EXLCZ)993790000000544837 100 $a20180105d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aParameter Estimation in Fractional Diffusion Models /$fby K?stutis Kubilius, Yuliya Mishura, Kostiantyn Ralchenko 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIX, 390 p. 17 illus., 2 illus. in color.) 225 1 $aBocconi & Springer Series, Mathematics, Statistics, Finance and Economics,$x2039-148X ;$v8 311 08$a9783319710297 311 08$a331971029X 320 $aIncludes bibliographical references and index. 327 $a1 Description and properties of the basic stochastic models -- 2 The Hurst index estimators for a fractional Brownian motion -- 3 Estimation of the Hurst index from the solution of a stochastic differential equation -- 4 Parameter estimation in the mixed models via power variations -- 5 Drift parameter estimation in diffusion and fractional diffusion models -- 6 The extended Orey index for Gaussian processes -- 7 Appendix A: Selected facts from mathematical and functional analysis -- 8 Appendix B: Selected facts from probability, stochastic processes and stochastic calculus. 330 $aThis book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years now, standard Brownian motion has been (and still remains) a popular model of randomness used to investigate processes in the natural sciences, financial markets, and the economy. The substantial limitation in the use of stochastic diffusion models with Brownian motion is due to the fact that the motion has independent increments, and, therefore, the random noise it generates is ?white,? i.e., uncorrelated. However, many processes in the natural sciences, computer networks and financial markets have long-term or short-term dependences, i.e., the correlations of random noise in these processes are non-zero, and slowly or rapidly decrease with time. In particular, models of financial markets demonstrate various kinds of memory and usually this memory is modeled by fractional Brownian diffusion. Therefore, the book constructs diffusion models with memory and provides simple and suitable parameter estimation methods in these models, making it a valuable resource for all researchers in this field. 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