Martin Wainwright

Martin Wainwright explores April Fools Day's development into a media phenomenon, collects all the best spoofs, revels in the gullibility that greets the tallest stories, and finds the most incredible stories published on this day that were actually true.

The Guardian Book of April Fool's Day

Commemorating the 100th anniversary of The Guardian's much-loved Country Diary column, Martin Wainwright has compiled a collection of the best of a century's writing, covering the landscape of the whole of the United Kingdom, from Wales to Northern Ireland, Scotland to Norfolk. The column has consistently attracted some of Britain's best writers on natural history and the countryside: Jim Perrin the mountaineering writer, whose biography of Don Whillans won the Boardman-Tasker Award, writes the dispatches from Snowdonia; Mark Cocker, author of Birders, writes the Country Diary from Norfolk. There are also diaries written by a leading Suffragette, one of Rupert Brooke's mistresses, and even one of The Guardian's printers. Martin Wainwright has found diaries to reflect the changing of the countryside over 100 years—from the prevalence of owls in World War I trenches full of vermin to the plant surveys of World War II bombsites.

100 Years of the Guardian Country Diary

A Gleaming Landscape

Designed by Sir Alec Issigonis back in 1948, the Morris Minor or Morris 1000 was in a sense Britain's answer to the Beetle. Here, Martin Wainwright provides a history of this quintessentially British car.

The Biography

Morris Minor

Abysmal weather, slag heaps, funny accents; the bleak uplands of a landscape carved out of millstone grit; townscapes of abandoned mills and shipyards; the detritus of an industrial revolution well past its sell-by date. These, all too often, are the gloomy perceptions of 'the north', the foundations for the belief that northerners spend their lives battling hardship and misery, and that nothing beyond Watford is worth a bag of chips. With an insider's sensitivity and a journalist's enquiring mind, northerner Martin Wainwright swiftly dispels these and other myths. He reaches back through the historical record to uncover where - and how - many of the old clichés arose, and goes on to paint a picture of the north as it is today and has always been: a setting of wild coastline, lakes, and green dales inhabited by indomitably inventive northeners, proud of their past and forging a future of brilliant new enterprises. Lavishly illustrated with over 100 stunning images from the Guardian's archives, Wainwright's incisive and wittily observant assessment of a region that is flourishing socially and culturally leaves us in no doubt that true north is as vibrant and exciting as it is beautiful.

In Praise of England's Better Half

True North

Marloes Maathuis

Mathias Drton

Steffen Lauritzen

A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art. Key features: * Contributions by leading researchers from a range of disciplines * Structured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applications * Balanced coverage of concepts, theory, methods, examples, and applications * Chapters can be read mostly independently, while cross-references highlight connections The handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.

Handbook of Graphical Models

Trevor Hastie

Robert Tibshirani

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

The Lasso and Generalizations

Statistical Learning with Sparsity

The classic high-level walk from Irish Sea to North Sea Originally devised by the legendary Alfred Wainwright, the Coast-to-Coast Walk has steadily become one of Britain’s most popular long-distance walks, and it is not hard to see why. Planned to seek out the most spectacular high ground across the country, it takes you from the sea in the west to the sea in the east via three of England’s loveliest National Parks: the Lake District, the Yorkshire Dales and the North York Moors. Its 192 miles, from the quiet Cumbrian village of St Bees to Robin Hood’s Bay, are a substantial test of endurance that will take even a fit walker a fortnight to accomplish, but also a wonderfully varying walk, from sea cliffs to craggy mountains and lakes, then across rolling dales and finally heather moorland. For everyone who reaches Wainwright’s Bar at journey’s end on the North Yorkshire coast it will have been the walk of a lifetime.

Coast to Coast Walk

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of l1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

Martin Wainwright

Books

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