Bandit Algorithms,Used

Bandit Algorithms,Used

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SKU: SONG1108486827
Brand: Cambridge University Press
Condition: Used
$53.96
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Decisionmaking in the face of uncertainty is a significant challenge in machine learning, and the multiarmed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multiarmed 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, nonstationary 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.

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This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.

For more information, please visit www.P65Warnings.ca.gov.

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