LEADER 03653oam 2200625 450 001 9910786101403321 005 20200301073150.0 010 $a1-136-28613-6 010 $a1-283-86206-9 010 $a1-136-28614-4 010 $a0-203-11329-2 024 7 $a10.4324/9780203113295 035 $a(CKB)2670000000312407 035 $a(EBL)1092774 035 $a(OCoLC)820787720 035 $a(SSID)ssj0000783058 035 $a(PQKBManifestationID)11442802 035 $a(PQKBTitleCode)TC0000783058 035 $a(PQKBWorkID)10753257 035 $a(PQKB)11585455 035 $a(MiAaPQ)EBC1092774 035 $a(Au-PeEL)EBL1092774 035 $a(CaPaEBR)ebr10632455 035 $a(CaONFJC)MIL417456 035 $a(OCoLC)897563525 035 $a(FINmELB)ELB134418 035 $a(UkLoBP)BP0065950337 035 $a(EXLCZ)992670000000312407 100 $a20180706d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDesigning to avoid disaster $ethe nature of fracture-critical design /$fThomas Fisher 210 1$aNew York :$cRoutledge,$d2012. 215 $a1 online resource (273 p.) 300 $aDescription based upon print version of record. 311 $a0-415-52736-8 311 $a0-415-52735-X 320 $aIncludes bibliographical references (p. [229]-246) and index. 327 $apt. 1. The nature of fracture-critical design -- pt. 2. How fracture-critical design affects our lives -- pt. 3. Designing to avoid future disasters. 330 $a"Recent catastrophic events, such as the I-35W bridge collapse, New Orleans flooding, the BP oil spill, Port au Prince's destruction by earthquake, Fukushima nuclear plant's devastation by tsunami, the Wall Street investment bank failures, and the housing foreclosure epidemic and the collapse of housing prices, all stem from what author Thomas Fisher calls fracture-critical design. This is design in which structures and systems have so little redundancy and so much interconnectedness and misguided efficiency that they fail completely if any one part does not perform as intended. If we, as architects, planners, engineers, and citizens are to predict and prepare for the next disaster, we need to recognize this error in our thinking and to understand how design thinking provides us with a way to anticipate unintended failures and increase the resiliency of the world in which we live. In Designing to Avoid Disaster, the author discusses the context and cultural assumptions that have led to a number of disasters worldwide, describing the nature of fracture-critical design and why it has become so prevalent. He traces the impact of fracture-critical thinking on everything from our economy and politics to our educational and infrastructure systems to the communities, buildings, and products we inhabit and use everyday. And he shows how the natural environment and human population itself have both begun to move on a path toward a fracture-critical collapse that we need to do everything possible to avoid. We designed our way to such disasters and we can design our way out of them, with a number of possible solutions that Fisher provides"--Provided by publisher. 606 $aDesign$xMethodology 606 $aSafety factor in engineering 615 0$aDesign$xMethodology. 615 0$aSafety factor in engineering. 676 $a620.86 700 $aFisher$b Thomas$f1953-$0885941 801 0$bUkLoBP 801 1$bUkLoBP 906 $aBOOK 912 $a9910786101403321 996 $aDesigning to avoid disaster$93727801 997 $aUNINA LEADER 04823nam 22006255 450 001 9911011656003321 005 20250625125948.0 010 $a9783031862748 024 7 $a10.1007/978-3-031-86274-8 035 $a(CKB)39450095700041 035 $a(MiAaPQ)EBC32176050 035 $a(Au-PeEL)EBL32176050 035 $a(OCoLC)1525619490 035 $a(DE-He213)978-3-031-86274-8 035 $a(EXLCZ)9939450095700041 100 $a20250625d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Learning in Genetics $eAn Introduction Using R /$fby Daniel Sorensen 205 $a2nd ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (1051 pages) 225 1 $aStatistics for Biology and Health,$x2197-5671 311 08$a9783031862731 327 $a- 1. Overview -- Part I: Fitting Likelihood and Bayesian Models -- 2. Likelihood -- 3. Computing the Likelihood -- 4. Bayesian Methods -- 5. McMC in Practice -- Part II: Prediction -- 6. Fundamentals of Prediction -- 7. Shrinkage Methods -- 8. Digression on Multiple Testing: False Discovery Rates -- 9. Binary Data -- 10. Bayesian Prediction and Model Checking -- 11. Nonparametric Methods: A Selected Overview -- Part III: Exercises and Solutions -- 12. Exercises -- 13. Solution to Exercises. 330 $aThis book provides an introduction to computer-based methods for the analysis of genomic data. Breakthroughs in molecular and computational biology have contributed to the emergence of vast data sets, where millions of genetic markers for each individual are coupled with medical records, generating an unparalleled resource for linking human genetic variation to human biology and disease. Similar developments have taken place in animal and plant breeding, where genetic marker information is combined with production traits. An important task for the statistical geneticist is to adapt, construct and implement models that can extract information from these large-scale data. An initial step is to understand the methodology that underlies the probability models and to learn the modern computer-intensive methods required for fitting these models. The objective of this book, suitable for readers who wish to develop analytic skills to perform genomic research, is to provide guidance to take this first step. This book is addressed to numerate biologists who may lack the formal mathematical background of the professional statistician. For this reason, considerably more detailed explanations and derivations are offered. Examples are used profusely and a large proportion involves programming with the open-source package R. The code needed to solve the exercises is provided and it can be downloaded, allowing students to experiment by running the programs on their own computer. Part I presents methods of inference and computation that are appropriate for likelihood and Bayesian models. Part II discusses prediction for continuous and binary data using both frequentist and Bayesian approaches. Some of the models used for prediction are also used for gene discovery. The challenge is to find promising genes without incurring a large proportion of false positive results. Therefore, Part II includes a detour on the False Discovery Rate, assuming frequentist and Bayesian perspectives. The last chapter of Part II provides an overview of a selected number of non-parametric methods. Part III consists of exercises and their solutions. This second edition has benefited from many clarifications and extensions of themes discussed in the first edition. Daniel Sorensen holds PhD and DSc degrees from the University of Edinburgh and is an elected Fellow of the American Statistical Association. He was professor of Statistical Genetics at Aarhus University where, at present, he is professor emeritus. 410 0$aStatistics for Biology and Health,$x2197-5671 606 $aStatistics 606 $aGenetics 606 $aQuantitative research 606 $aBiometry 606 $aStatistical Theory and Methods 606 $aGenetics 606 $aData Analysis and Big Data 606 $aBiostatistics 615 0$aStatistics. 615 0$aGenetics. 615 0$aQuantitative research. 615 0$aBiometry. 615 14$aStatistical Theory and Methods. 615 24$aGenetics. 615 24$aData Analysis and Big Data. 615 24$aBiostatistics. 676 $a576.5015195 700 $aSorensen$b Daniel$01429691 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911011656003321 996 $aStatistical Learning in Genetics$93568944 997 $aUNINA