Search results for "survival analysis with python"
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Proceedings of the 8th Python in Science Conference
The proceedings of the 8th annual Python for Scientific Computing conference.
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Factors Influencing Child Survival in Tanzania
This book addresses the essential topic of child survival in Tanzania, especially focusing on the role of mutual assistance, which has received little attention to date. Further, it identifies a range of key factors for child survival by combining a literature review, regional data analysis, and case studies. These studies center on rural villages in high Under-5 mortality rate (U5MR) regions and assess their strengths and weaknesses regarding child survival. By focusing on deprived rural areas as of 2002 and evaluating the improvements in the 2012 census data, the book also highlights the potential held by rural semi -subsistence economies. An analysis of the focus villages indicates that children in food-sharing circles had better chances of survival. However, food sharing is not necessarily inclusive; a significant number of children have fallen out of such circles, especially in mainland villages. Furthermore, monetary support for children’s medicine has often failed to arrive in time. Lastly, the book argues that, in addition to direct factors such as access to health services, water and sanitation, food intake, and education, it is essential that children receive inclusive support at various levels: family, community, village, national, and international.
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Software for Data Analysis
John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. This is the only advanced programming book on R, written by the author of the S language from which R evolved.
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Numerical Methods in Chemical Engineering Using Python® and Simulink®
Numerical methods are vital to the practice of chemical engineering, allowing for the solution of real-world problems. Written in a concise and practical format, this textbook introduces readers to the numerical methods required in the discipline of chemical engineering and enables them to validate their solutions using both Python and Simulink.Introduces numerical methods, followed by the solution of linear and nonlinear algebraic equationsDeals with the numerical integration of a definite function and solves initial and boundary value ordinary differential equations with different ordersWeaves in examples of various numerical methods and validates solutions to each with Python and Simulink graphical programmingFeatures appendices on how to use Python and SimulinkAimed at advanced undergraduate and graduate chemical engineering students, as well as practicing chemical engineers, this textbook offers a guide to the use of two of the most widely used programs in the discipline.The textbook features numerous video lectures of applications and a solutions manual for qualifying instructors.
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Coercion, Survival, and War
In asymmetric interstate conflicts, great powers have the capability to coerce weak states by threatening their survival—but not vice versa. It is therefore the great power that decides whether to escalate a conflict into a crisis by adopting a coercive strategy.In practice, however, the coercive strategies of the U.S. have frequently failed. In Coercion, Survival and War Phil Haun chronicles 30 asymmetric interstate crises involving the US from 1918 to 2003. The U.S. chose coercive strategies in 23 of these cases, but coercion failed half of the time: most often because the more powerful U.S. made demands that threatened the very survival of the weak state, causing it to resist as long as it had the means to do so. It is an unfortunate paradox Haun notes that, where the U.S. may prefer brute force to coercion, these power asymmetries may well lead it to first attempt coercive strategies that are expected to fail in order to justify the war it desires.He concludes that, when coercion is preferred to brute force there are clear limits as to what can be demanded. In such cases, he suggests, U.S. policymakers can improve the chances of success by matching appropriate threats to demands, by including other great powers in the coercive process, and by reducing a weak state leader's reputational costs by giving him or her face-saving options.
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Sparse Estimation with Math and Python
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building Python programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers’ insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter.This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.This book is one of a series of textbooks in machine learning by the same Author. Other titles are: Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679)Statistical Learning with Math and Pyth (https://www.springer.com/gp/book/9789811578762)Sparse Estimation with Math and R
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