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نمایش تعداد 1-7 از 7

Stochastic Learning and Optimization: A Sensitivity-Based Approach

نوع: Ebook

نویسنده : Xi-Ren Cao

ناشر: Springer

سال: 2007

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Stochastic Learning and Optimization: A Sensitivity-Based Approach (International Series on Discrete Event Dynamic Systems)

نوع: Ebook

نویسنده : Xi-Ren Cao

ناشر: Springer

سال: 2007

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Markov Decision Processes

نوع: Journal Paper

نویسنده : Xi-Ren Cao

سال: 2007

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Stochastic Learning and Optimization: A Sensitivity-Based Approach

نوع: Ebook

نویسنده : Xi-Ren Cao PhD (auth.)

ناشر: Springer US

سال: 2007

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Perturbation Analysis of Discrete Event Dynamic Systems

نوع: Ebook

نویسنده : Yu-Chi Ho; Xi-Ren Cao (auth.)

ناشر: Springer US

سال: 1991

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Realization Probabilities: The Dynamics of Queuing Systems

نوع: Ebook

نویسنده : Xi-Ren Cao PhD (eds.)

ناشر: Springer Berlin Heidelberg

سال: 1994

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Partial-Information State-Based Optimization of Partially Observable Markov Decision Processes and the Separation Principle

نوع: Journal Paper

نویسنده : Xi-Ren Cao; De-Xin Wang; Li Qiu

ناشر: IEEE

سال: 2014

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Stochastic Learning and Optimization: A Sensitivity-Based Approach

Stochastic Learning and Optimization: A Sensitivity-Based Approach (International Series on Discrete Event Dynamic Systems)

Markov Decision Processes

Stochastic Learning and Optimization: A Sensitivity-Based Approach

Perturbation Analysis of Discrete Event Dynamic Systems

Realization Probabilities: The Dynamics of Queuing Systems

Partial-Information State-Based Optimization of Partially Observable Markov Decision Processes and the Separation Principle

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Stochastic Learning and Optimization: A Sensitivity-Based Approach

Stochastic Learning and Optimization: A Sensitivity-Based Approach (International Series on Discrete Event Dynamic Systems)

Markov Decision Processes

Stochastic Learning and Optimization: A Sensitivity-Based Approach

Perturbation Analysis of Discrete Event Dynamic Systems

Realization Probabilities: The Dynamics of Queuing Systems

Partial-Information State-Based Optimization of Partially Observable Markov Decision Processes and the Separation Principle