In maximum entropy sampling (MES), a design is chosen by maximizing the joint Shannon entropy of parameters and observations. However, when the conditional parametric model of the response contains a large number of covariates, the posterior calculations in MES can be challenging or infeasible. In this work, we consider the use of composite likelihood modelling to break down the complexity of the full likelihood and code the original optimization problem into a set of simple partial likelihood problems. We study the optimality behaviour of the composite likelihood sampling approach as the number of design variables grows using both asymptotic analysis and numerical simulations.
Ferrari, D., Borrotti, M. (2013). Maximum Entropy Design in High Dimensions by Composite Likelihood Modelling. In mODa 10 – Advances in Model-Oriented Design and Analysis (pp.73-80) [10.1007/978-3-319-00218-7_9].
Maximum Entropy Design in High Dimensions by Composite Likelihood Modelling
Borrotti, M
2013
Abstract
In maximum entropy sampling (MES), a design is chosen by maximizing the joint Shannon entropy of parameters and observations. However, when the conditional parametric model of the response contains a large number of covariates, the posterior calculations in MES can be challenging or infeasible. In this work, we consider the use of composite likelihood modelling to break down the complexity of the full likelihood and code the original optimization problem into a set of simple partial likelihood problems. We study the optimality behaviour of the composite likelihood sampling approach as the number of design variables grows using both asymptotic analysis and numerical simulations.
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