Optimal worst case estimation for LPV–FIR models with bounded errors

Optimal worst case estimation for LPV-FIR models with bounded errors

In this paper discrete time linear parameter varying (LPV) models with nite impulse response (FIR) dynamic structure are considered. Measurement errors are assumed to be bounded and in such condition the worst case parameter estimate errors are derived together with the input sequences that allow their determination. The main result of the paper consists in showing that the optimal input design...

Near Optimal Routing Lookups with Bounded Worst Case Performance

The problem of route address lookup has received much attention recently and several algorithms and data structures for performing address lookups at high speeds have been proposed. In this paper we consider one such data structure – a binary search tree built on the intervals created by the routing table prefixes. We wish to exploit the difference in the probabilities with which the various le...

Worst-case estimation for econometric models with unobservable components

A worst-case estimator for econometric models containing unobservable components, based on minimax principles for optimal selection of parameters, is proposed.Worst-case estimators are robust against the averse effects of unobservables. Computing worstcase estimators involves solving aminimax continuous problem,which is quite a challenging task. Large sample theory is considered, and a Monte Ca...

Appendix: Worst-case Optimal Submodular Extensions for Marginal Estimation

Worst-case Optimal Submodular Extensions for Marginal Estimation

Submodular extensions of an energy function can be used to efficiently compute approximate marginals via variational inference. The accuracy of the marginals depends crucially on the quality of the submodular extension. To identify the best possible extension, we show an equivalence between the submodular extensions of the energy and the objective functions of linear programming (LP) relaxation...