Browsing by Author "Fu, Michael"
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Item Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise(PLOS Computational Biology, 2020-08-02) Presacco, Alessandro; Miran, Sina; Fu, Michael; Marcus, Steven; Jonathan, Simon; Babadi, BetashMEG data used for the "Switching attention" experimentItem Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise(PLOS Computational Biology, 2020-08-02) Presacco, Alessandro; Miran, Sina; Fu, Michael; Marcus, Steven; Simon, Jonathan; Babadi, BehtashMEG data used for the "Switching attention" experiment. This set of data refers to the part of the "forced" switching of attentionItem Online Appendix for “Gradient-Based Myopic Allocation Policy: An Efficient Sampling Procedure in a Low-Confidence Scenario”(2017) Peng, Yijie; Chen, Chun-Hung; Fu, Michael; Hu, Jian-QiangThis is the online appendix, which includes theoretical and numerical supplements containing some technical details and three additional numerical examples, which could not fit in the main body due to page limits by the journal for a technical note. The abstract for the main body is as follows: In this note, we study a simulation optimization problem of selecting the alternative with the best performance from a finite set, or a so-called ranking and selection problem, in a special low-confidence scenario. The most popular sampling allocation procedures in ranking and selection do not perform well in this scenario, because they all ignore certain induced correlations that significantly affect the probability of correct selection in this scenario. We propose a gradient-based myopic allocation policy (G-MAP) that takes the induced correlations into account, reflecting a trade-off between the induced correlation and the two factors (mean-variance) found in the optimal computing budget allocation formula. Numerical experiments substantiate the efficiency of the new procedure in the low-confidence scenario.Item Online Supplement to `Efficient Simulation Resource Sharing and Allocation for Selecting the Best'(2012) Peng, Yijie; Chen, Chun-Hung; Fu, Michael; Hu, Jian-QiangThis is the online supplement to the article by the same authors, "Efficient Simulation Resource Sharing and Allocation for Selecting the Best," published in the IEEE Transactions on Automatic Control.Item Online Supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate’(2016) Peng, Yijie; Fu, MichaelIn this online appendix, we test the performance of the AOMAP (asymptotically optimal myopic allocation policy) algorithm under the unknown variances scenario and compare it with EI (expected improvement) and OCBA (optimal computing budget allocation).