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Markov chain Monte Carlo for incomplete information discrete games
Misra, Sanjog - In: Quantitative marketing and economics : QME 11 (2013) 1, pp. 117-153
Persistent link: https://www.econbiz.de/10009744663
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Bayesian estimation of discrete games of complete information
Narayanan, Sridhar - In: Quantitative marketing and economics : QME 11 (2013) 1, pp. 39-81
Persistent link: https://www.econbiz.de/10009744666
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Bayesian analysis of entry games : a simulated likelihood approach without simulation errors
Jo, Jinhee; Seo, Kyoungwon - In: Global economic review 45 (2016) 3, pp. 294-309
Persistent link: https://www.econbiz.de/10011566033
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Bayesian estimation of discrete games of complete information
Narayanan, Sridhar - 2011
Persistent link: https://www.econbiz.de/10009269431
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Bayesian Estimation of Discrete Games of Complete Information
Narayanan, Sridhar - 2011
, leading to the absence of a one-to-one mapping between parameters and outcomes. Theory typically has little to say about …
Persistent link: https://www.econbiz.de/10014181616
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Uncertainty Quantification with R : Bayesian Methods
Cursi, Eduardo Souza de - 2024
This book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a...
Persistent link: https://www.econbiz.de/10014529692
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Strong convergence and dynamic economic models
Bray, Robert L. - In: Quantitative economics : QE ; journal of the … 10 (2019) 1, pp. 43-65
Morton and Wecker (1977) stated that the value iteration algorithm solves a dynamic program's policy function faster than its value function when the limiting Markov chain is ergodic. I show that their proof is incomplete, and provide a new proof of this classic result. I use this result to...
Persistent link: https://www.econbiz.de/10011994824
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Robust data sampling in machine learning : a game-theoretic framework for training and validation data selection
Mo, Zhaobin; Di, Xuan; Shi, Rongye - In: Games 14 (2023) 1, pp. 1-13
dataset is heterogeneous and skewed. In this paper, we propose a data sampling method that robustly selects training …/validation data. We formulate the training/validation data sampling process as a two-player game: a trainer aims to sample training … error. Robust sampling is achieved at the game equilibrium. To accelerate the searching process, we adopt reinforcement …
Persistent link: https://www.econbiz.de/10014420298
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Robustness and sample complexity of model-based marl for general-sum markov games
Subramanian, Jayakumar; Sinha, Amit; Mahajan, Aditya - In: Dynamic games and applications : DGA 13 (2023) 1, pp. 56-88
Persistent link: https://www.econbiz.de/10014225776
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The price of incentivizing exploration : a characterization via Thompson sampling and sample complexity
Sellke, Mark; Slivkins, Aleksandrs - In: Operations research 71 (2023) 5, pp. 1706-1732
Persistent link: https://www.econbiz.de/10014393168