Symbolizing luxury and old-fashioned glamour, Monte Carlo is a glorious Eden, perfectly manicured and architecturally grand. From its ancient Phoenician origins to its burgeoning power as a global financial center, this gorgeous volume chronicles the Grimaldi dynasty, the artists and socialists who first put the Principality on the map and how Hollywood darling Grace Kelly attracted the celebrity spotlight, the vibrant arts and culture scene, legendary Grand Prix race, and the controversial metamorphosis of the cityscape"--Jacket.

"Drawing on letters, correspondence, oral histories, and interviews, Baronova's daughter, the actress Victoria Tennant, ... recounts Baronova's dramatic life, from her earliest aspirations to her grueling time on tour to her later years in Australia as a pioneer of the art"--Dust jacket flap.

This book expands the topic of Monte Carlo simulation for graduate students and researchers in physics.

"A rollicking narrative history of Jazz Age Monte Carlo, chronicling the city's rise from WWI's ashes to become one of the world's most storied, infamous playgrounds of the rich, only to be crushed under it's own weight ten years later"--Provided by publisher.

The legend of St. Tropez starts with a dog, a rooster, and a martyr; and it leads to movie stars, world-renowned artists and distinguished writers. Located on the sparkling French Riviera, St. Tropez has enjoyed the spotlight for more than half a century, for better or worse, with celebrities flocking to this idyllic locale for its beaches and a dose of Mediterranean sun. A picturesque oasis, St. Tropez has served as inspiration for a who’s who of notable writers from Françoise Sagan to Colette; as well as renowned artists Paul Signac and Henri Matisse; and even filmmakers. However, St. Tropez would not be the same without then belle du jour Brigitte Bardot, her films and lovers and many other famous couples including Annabel and Bernard Buffet and Bianca and Mick Jagger.

Dealing with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed matter physics and statistical mechanics, this book provides an introduction to computer simulations in physics. The 5th edition contains extensive new material describing numerous powerful algorithms and methods that represent recent developments in the field. New topics such as active matter and machine learning are also introduced. Throughout, there are many applications, examples, recipes, case studies, and exercises to help the reader fully comprehend the material. This book is ideal for graduate students and researchers, both in academia and industry, who want to learn techniques that have become a third tool of physical science, complementing experiment and analytical theory.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

The Monte Carlo Method: The Method of Statistical Trials is a systematic account of the fundamental concepts and techniques of the Monte Carlo method, together with its range of applications. Some of these applications include the computation of definite integrals, neutron physics, and in the investigation of servicing processes. This volume is comprised of seven chapters and begins with an overview of the basic features of the Monte Carlo method and typical examples of its application to simple problems in computational mathematics. The next chapter examines the computation of multi-dimensional integrals using the Monte Carlo method. Some examples of statistical modeling of integrals are analyzed, together with the accuracy of the computations. Subsequent chapters focus on the applications of the Monte Carlo method in neutron physics; in the investigation of servicing processes; in communication theory; and in the generation of uniformly distributed random numbers on electronic computers. Methods for organizing statistical experiments on universal digital computers are discussed. This book is designed for a wide circle of readers, ranging from those who are interested in the fundamental applications of the Monte Carlo method, to those who are concerned with comparatively limited problems of the peculiarities of simulating physical processes.