£40.48

Cambridge University Press Bandit Algorithms

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£40.48 £33.11 £34.72 £36.33 £37.93 £39.54 £41.15 10 February 2026 04 March 2026 26 March 2026 17 April 2026 10 May 2026

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Description

Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.

Key Features

New Store Stock

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
16 July 2020
Listed Since
28 November 2019

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No barcode data available

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