Search Results - subject:"Bayesian Dynamic Programming"

Bayesian inventory control : accelerated demand learning via exploration boosts

Chuang, Ya-Tang; Kim, Michael Jong - In: Operations research 71 (2023) 5, pp. 1515-1529

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014393149

Learning manipulation through information dissemination

Keppo, Jussi; Kim, Michael Jong; Zhang, Xinyuan - In: Operations research 70 (2022) 6, pp. 3490-3510

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014307893

Variance regularization in sequential Bayesian optimization

Kim, Michael Jong - In: Mathematics of operations research 45 (2020) 3, pp. 966-992

Persistent link: https://ebvufind01.dmz1.zbw.eu/10012293363

A practitioner's guide to Bayesian estimation of discrete choice dynamic programming models

Ching, Andrew; Imai, Susumu; Ishihara, Masakazu; Jain, … - 2009

Bayesian Dynamic Programming algorithm developed in Imai, Jain and Ching (2008) (IJC). The IJC method combines the DDP solution … information obtained in the past iterations remains unused in the current iteration. In contrast, the Bayesian Dynamic Programming …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010290365

A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models

Ching, Andrew; Imai, Susumu; Ishihara, Masakazu; Jain, … - Economics Department, Queen's University - 2009

Bayesian Dynamic Programming algorithm developed by Imai Jain and Ching (2008) (IJC). The IJC method combines the DDP solution … information obtained in the past iterations remains unused in the current iteration. In contrast, the Bayesian Dynamic Programming …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005209120

Bayesian Estimation of Dynamic Discrete Choice Models

Imai, Susumu; Jain, Neelam; Ching, Andrew - 2006

We propose a new methodology for structural estimation of dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011940732

Bayesian Estimation of Dynamic Discrete Choice Models

Imai, Susumu; Jain, Neelam; Ching, Andrew - Economics Department, Queen's University - 2006

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005688258

Dynamic Inventory Management with Learning About the Demand Distribution and Substitution Probability

Chen, Li; Plambeck, Erica L. - In: Manufacturing & Service Operations Management 10 (2008) 2, pp. 236-256

Awell-known result in the Bayesian inventory management literature is: If lost sales are not observed, the Bayesian optimal inventory level is larger than the myopic inventory level (one should "stock more" to learn about the demand distribution). This result has been proven in other studies...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10009218851

Sensitivity analysis of a sequential decision problem with learning

Müller, Alfred; Scarsini, Marco - In: Computational Statistics 57 (2003) 2, pp. 321-327

decision maker. Using techniques from Bayesian dynamic programming we will show that under the assumption of a beta …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010847840

Sensitivity analysis of a sequential decision problem with learning

Müller, Alfred; Scarsini, Marco - In: Mathematical Methods of Operations Research 57 (2003) 2, pp. 321-327

decision maker. Using techniques from Bayesian dynamic programming we will show that under the assumption of a beta …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010999849