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Articles 1 - 3 of 3
Full-Text Articles in Design of Experiments and Sample Surveys
Approximating Bayesian Optimal Sequential Designs Using Gaussian Process Models Indexed On Belief States, Joseph Burris
Approximating Bayesian Optimal Sequential Designs Using Gaussian Process Models Indexed On Belief States, Joseph Burris
Theses and Dissertations
Fully sequential optimal Bayesian experimentation can offer greater utility than both traditional Bayesian designs and greedy sequential methods, but practically cannot be solved due to numerical complexity and continuous outcome spaces. Approximate solutions can be found via approximate dynamic programming, but rely on surrogate models of the expected utility at each trial of the experiment with hand-chosen features or use methods which ignore the underlying geometry of the space of probability distributions. We propose the use of Gaussian process models indexed on the belief states visited in experimentation to provide utility-agnostic surrogate models for approximating Bayesian optimal sequential designs which …
A Markov Decision Process Approach To Adaptive Contact Strategies, Artur Grygorian
A Markov Decision Process Approach To Adaptive Contact Strategies, Artur Grygorian
College of Graduate Studies: Theses & Dissertations
In the field of survey methodology, optimizing contact strategies helps organizations increase response rates using their allocated budget. Markov Decision Processes (MDP) are widely used to model decision-making strategies in situations where the outcomes have a random component. In this research, we use MDPs and adaptive sampling techniques to construct a strategy that, based on target audience characteristics, suggests the best contact policy. The data we use comes from the First Destination Survey conducted by the Office of Career Services at Georgia Southern University. The constructed model is quite flexible and can be used by other organizations to optimize their …
A Markov Decision Process Approach To Optimal Control Of A Multi-Level Hierarchical Manpower System, Akaninyene U. Udom
A Markov Decision Process Approach To Optimal Control Of A Multi-Level Hierarchical Manpower System, Akaninyene U. Udom
CBN Journal of Applied Statistics (JAS)
A recurrent problem in manpower control is how to attain the desired structural configuration in an optimal way, since it is possible to reach a desired structural configuration using different control inputs. The major aim of this paper is to develop a Markov Decision Process for optimal control of a Multi-level Hierarchical Manpower System (MHMS) by promotion and interdepartmental transfers. This is examined under control by intervention and contraction cost Markov Decision Process.