Why did the main challenge to the Ottoman state come not in peasant or elite rebellions, but in endemic banditry? Karen Barkey shows how Turkish strategies of incorporating peasants and rotating elites kept both groups dependent on the state, unable and unwilling to rebel. Bandits, formerly mercenary soldiers, were not interested in rebellion but concentrated on trying to gain state resources, more as rogue clients than as primitive rebels. The state's ability to control and manipulate bandits—through deals, bargains and patronage—suggests imperial strength rather than weakness, she maintains. Bandits and Bureaucrats details, in a rich, archivally based analysis, state-society relations in the Ottoman empire during the sixteenth and seventeenth centuries. Exploring current eurocentric theories of state building, the author illuminates a period often mischaracterized as one in which the state declined in power. Outlining the processes of imperial rule, Barkey relates the state political and military institutions to their socal foundations. She compares the Ottoman route with state centralization in the Chinese and Russian empires, and contrasts experiences of rebellion in France during the same period. Bandits and Bureaucrats thus develops a theoretical interpretation of imperial state centralization through incorporation and bargaining with social groups, and at the same time enriches our understanding of the dynamics of Ottoman history.

In 1989 the first edition of this book set out Gittins' pioneering index solution to the multi-armed bandit problem and his subsequent investigation of a wide of sequential resource allocation and stochastic scheduling problems. Since then there has been a remarkable flowering of new insights, generalizations and applications, to which Glazebrook and Weber have made major contributions. This second edition brings the story up to date. There are new chapters on the achievable region approach to stochastic optimization problems, the construction of performance bounds for suboptimal policies, Whittle's restless bandits, and the use of Lagrangian relaxation in the construction and evaluation of index policies. Some of the many varied proofs of the index theorem are discussed along with the insights that they provide. Many contemporary applications are surveyed, and over 150 new references are included. Over the past 40 years the Gittins index has helped theoreticians and practitioners to address a huge variety of problems within chemometrics, economics, engineering, numerical analysis, operational research, probability, statistics and website design. This new edition will be an important resource for others wishing to use this approach.

A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.

Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. This book provides four in-depth sections that cover all aspects of machine learning engineering: Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines

In the early twentieth century, social banditry was endemic in the countryside near the border between the northern Malaysian state of Kedah and Siam, and some outlaws became local heroes. Cheah Boon Kheng's account of peasant banditry and the society where it flourished draws on colonial records, literary sources and interviews to examine the circumstances that led the Governor, Sir Laurence Guillemard, to call the border area "one of the most lawless and insecure districts" in British Malaya during the 1920s. Considering banditry from the perspective of the peasant community, Cheah concludes that it grew out of lax government, weak policing, the geography of the border region and underdevelopment, and suggests that bandit heroes might be seen as symbols of rural protest. His discussion of the details of rural life in the early twentieth century and the conditions that underlay rural crime provide a unique social history of rural society in Malaya. This innovative volume broke new ground in Malaysian studies when it first appeared in 1988. This second edition is intended for the work to reach a new audience.

Bandits seem ubiquitous in Latin American culture. Even contemporary actors of violence are framed by narratives that harken back to old images of the rural bandit, either to legitimize or delegitimize violence, or to intervene in larger conflicts within or between nation-states. However, the bandit seems to escape a straightforward definition, since the same label can apply to the leader of thousands of soldiers (as in the case of Villa) or to the humble highwayman eking out a meager living by waylaying travelers at machete point. Dabove presents the reader not with a definition of the bandit, but with a series of case studies showing how the bandit trope was used in fictional and non-fictional narratives by writers and political leaders, from the Mexican Revolution to the present. By examining cases from Argentina, Brazil, Mexico, Peru, and Venezuela, from Pancho Villa's autobiography to Hugo Chavez's appropriation of his "outlaw" grandfather, Dabove reveals how bandits function as a symbol to expose the dilemmas or aspirations of cultural and political practices, including literature as a social practice and as an ethical experience.

When looking for ways to improve your website, how do you decide which changes to make? And which changes to keep? This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site. Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success. This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through code examples written in Python, which you can easily adapt for deployment on your own website. Learn the basics of A/B testing—and recognize when it’s better to use bandit algorithms Develop a unit testing framework for debugging bandit algorithms Get additional code examples written in Julia, Ruby, and JavaScript with supplemental online materials