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This paper introduces a new solution concept for games with incomplete preferences. The concept is based on rationalizability and it is more general than the existing ones based on Nash equilibrium. In rationalizable strategies, we assume that the players choose nondominated strategies given their beliefs of what strategies the other players may choose. Our solution concept can also be used, e.g.,...
We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown parameters and present an algorithm for computationally efficient inference on the resulting model. The proposed algorithm is based on combining the...
This paper is concerned with the use of split-Gaussian importance distributions in sequential importance resampling based particle filtering. We present novel particle filtering algorithms using the split-Gaussian importance distributions and compare their performance with several alternatives. Using a univariate nonlinear reference model, we compare the performance off the importance distributions...
We consider parameter estimation in non-linear state space models by using expectation-maximization based numerical approximations to likelihood maximization. We present a unified view of approximative EM algorithms that use either sigma-point or particle smoothers to evaluate the integrals involved in the expectation step of the EM method, and compare these methods to direct likelihood maximization...
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