9781009449434 - Partially Observed Markov Decision Processes: Filtering, Learning and Controlled Sensing de Krishnamurthy, Vikram (21 resultados)

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Condición: New. 2nd edition NO-PA16APR2015-KAP.

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Hardback. Condición: New. Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction.

Hardcover. Condición: new. Hardcover. Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction. This survey of formulation, algorithms, and structural results in POMDPs focuses on underlying concepts and connections to real-world applications in controlled sensing, keeping technical machinery to a minimum. The new edition includes inverse reinforcement learning, non-parametric Bayesian inference, variational Bayes and conformal prediction. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condición: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.

Hardcover. Condición: Brand New. 2nd edition. 651 pages. 7.00x1.38x10.00 inches. In Stock.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction.

Hardback. Condición: New. Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction.

Hardcover. Condición: new. Hardcover. Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction. This survey of formulation, algorithms, and structural results in POMDPs focuses on underlying concepts and connections to real-world applications in controlled sensing, keeping technical machinery to a minimum. The new edition includes inverse reinforcement learning, non-parametric Bayesian inference, variational Bayes and conformal prediction. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condición: Brand New. 2nd edition. 651 pages. 7.00x1.38x10.00 inches. In Stock. This item is printed on demand.

Buch. Condición: Neu. Partially Observed Markov Decision Processes | Vikram Krishnamurthy | Buch | Englisch | 2025 | Cambridge University Press | EAN 9781009449434 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Hardcover. Condición: new. Hardcover. Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction. This survey of formulation, algorithms, and structural results in POMDPs focuses on underlying concepts and connections to real-world applications in controlled sensing, keeping technical machinery to a minimum. The new edition includes inverse reinforcement learning, non-parametric Bayesian inference, variational Bayes and conformal prediction. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.