Conditional Design Workbook

Conditional design is a design method formulated by the graphic designers Luca Maurer, Jonathan Puckey, Roel Wouters and the artist Edo Paulus, in which conditions and rules of play are drawn up that invite cooperation within a 'regulated' process towards an unpredictable design or result.

Conditional design is the sequel to Operative Design. This book will further explore the operative in a more detailed, intentional, and perhaps functional manner. Spatially, the conditional is the result of the operative. It is not a blind result however. Both terms work together to satisfy a formal manipulation through a set of opportunities for elements such as connections and apertures.

From a leading voice on recovering from toxic relationships, a deeply insightful guide to getting back to your "old self" again--in order to truly heal and move on. Jackson MacKenzie has helped millions of people in their struggle to understand the experience of toxic relationships. His first book, Psychopath Free, explained how to identify and survive the immediate situation. In this highly anticipated new book, he guides readers on what to do next--how to fully heal from abuse in order to find love and acceptance for the self and others. Through his close work with--and deep connection to--thousands of survivors of abusive relationships Jackson discovered that most survivors have symptoms of trauma long after the relationship is over. These range from feelings of numbness and emptiness to depression, perfectionism, substance abuse, and many more. But he’s also found that it is possible to work through these symptoms and find love on the other side, and this book shows how. Through a practice of mindfulness, introspection, and exercises using specific tools, readers learn to identify the protective self they've developed - and uncover the core self, so that they can finally move on to live a full and authentic life--to once again feel light, free, and whole, and ready to love again. This book addresses and provides crucial guidance on topics and conditions like: complex PTSD, Narcissistic abuse, Avoidant Personality Disorder, Codependency, Core wounding, toxic shame, Borderline Personality Disorder, and so many more. Whole Again offers hope and multiple strategies to anyone who has survived a toxic relationship, as well as anyone suffering the effects of a breakup involving lying, cheating and other forms of abuse--to release old wounds and safely let the love back inside where it belongs.

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

& Most software practitioners deal with inherited code; this book teaches them how to optimize it & & Workbook approach facilitates the learning process & & Helps you identify where problems in a software application exist or are likely to exist

According to the great mathematician Paul Erdös, God maintains perfect mathematical proofs in The Book. This book presents the authors candidates for such "perfect proofs," those which contain brilliant ideas, clever connections, and wonderful observations, bringing new insight and surprising perspectives to problems from number theory, geometry, analysis, combinatorics, and graph theory. As a result, this book will be fun reading for anyone with an interest in mathematics.

Designed as a project and case-oriented approach to learning Excel, the emphasis of this book is on learning by doing. The book presents a series of progressively reinforcing problem sets, which allow the exploration of real-life business problems. First, the background, theory, formulas, and calculations are discussed, followed by the design of Excel spreadsheets, which support the creation of effective spreadsheets for these applications. Finally, the proper solution and other related aspects are discussed, forming a cohesive set of practical application problems. Some of the topics explored include amortization tables, weighted averages, cash flows, payroll calculations, break even analysis, and spreadsheet databases. Excel techniques include formulas and functions, cell addressing, conditional and lookup functions, graphs, sorting, and filtering. FEATURES: Provides 30 projects, several How-to Guides, and Application Types to learn Excel skills using problems, applications, and case studies featuring practical business problems Explores formulas and functions, financial functions, cell addressing, conditional functions, lookup functions, graphs, sorting, and filtering, amortization tables, future values of an investment, weighted averages, cash flows, payroll calculations, break even analysis, economic order quantity, spreadsheet databases, and more Instructor resources available with four Excel video tutorials and images from the text.

Discrete optimization problems are everywhere, from traditional operations research planning problems, such as scheduling, facility location, and network design; to computer science problems in databases; to advertising issues in viral marketing. Yet most such problems are NP-hard. Thus unless P = NP, there are no efficient algorithms to find optimal solutions to such problems. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first part of the book is devoted to a single algorithmic technique, which is then applied to several different problems. The second part revisits the techniques but offers more sophisticated treatments of them. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithms courses, the book will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.