Surrogates

Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc.

Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc.

Surrogates is a graduate textbook, on topics at the interface between machine learning, spatial statistics, computer simulation, meta-modeling, design of experiments, and optimization. Experimentation through simulation, management of dynamic processes, online and real-time analysis, automation and practical application are at the forefro

If you need help having a baby, reproductive technology can supply the answer. But it also raises a host of questions that won’t arise until after the child is born: What will you say to “Where did I come from?” when the answer includes a donor or surrogate? Will knowing the truth about how you conceived make your child love you less? Will having a baby with someone else strain your relationship with your spouse or partner? What will grandparents, family members, friends, and coworkers think? Dr. Diane Ehrensaft--a developmental and clinical psychologist who’s worked with families formed using assisted reproductive technology for more than 20 years--helps you anticipate the big questions and find solutions that are right for you and your loved ones. Dr. Ehrensaft offers information, support, and straightforward advice for coping with private worries, confronting public prejudices, and raising happy, healthy children. Single or married, straight or gay, anyone looking forward to the joys and challenges of building a family with the help of a donor or surrogate will discover a wealth of thought-provoking ideas and fresh insights in this sensitive, practical, and positive book.

"The prequel to the hit sci-fi graphic novel"--Cover.

Reprint of the original, first published in 1875.

Did she choose that?’ Or, more normatively, ’Why would she choose that?’ This book critiques and offers an alternative to these questions, which have traditionally framed law and policy discussions circulating around controversial genderized practices. It examines the simplicity and incompleteness of choice-based rhetoric and of presumptions that women’s conduct is shaped, in an absolute way, either by choice or by coercion. This book develops an analytical framework that aims to discern the meaning and value that women may ascribe to morally ambiguous practices. An analysis of law’s approach to polygamy, surrogacy and sex work, particularly in Canada, the United Kingdom and Australia, provides a basis for evaluating the choice-coercion binary and for contemplating alternate modes for assessing, from a law and policy standpoint, the palatability of social practices that appear pernicious to women. Weaving together interdisciplinary research, an innovative analytical framework for assessing choices ostensibly harmful to women, and a critique of the legal rules governing such choices, this book bears relevance for students, scholars, practicing jurists and policymakers seeking a richer understanding of conduct that moves women to the margins of law and society.

Birthing a Mother is the first ethnography to probe the intimate experience of gestational surrogate motherhood. In this beautifully written and insightful book, Elly Teman shows how surrogates and intended mothers carefully negotiate their cooperative endeavor. Drawing on anthropological fieldwork among Jewish Israeli women, interspersed with cross-cultural perspectives of surrogacy in the global context, Teman traces the processes by which surrogates relinquish any maternal claim to the baby even as intended mothers accomplish a complicated transition to motherhood. Teman’s groundbreaking analysis reveals that as surrogates psychologically and emotionally disengage from the fetus they carry, they develop a profound and lasting bond with the intended mother.