Academic literature on the topic 'Kullback-leibler average'
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Journal articles on the topic "Kullback-leibler average"
Luan, Yu, Hong Zuo Li, and Ya Fei Wang. "Acoustic Features Selection of Speaker Verification Based on Average KL Distance." Applied Mechanics and Materials 373-375 (August 2013): 629–33. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.629.
Full textLu, Wanbo, and Wenhui Shi. "Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model." Complexity 2022 (April 27, 2022): 1–13. http://dx.doi.org/10.1155/2022/7706992.
Full textNielsen, Frank. "On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means." Entropy 21, no. 5 (May 11, 2019): 485. http://dx.doi.org/10.3390/e21050485.
Full textBattistelli, Giorgio, and Luigi Chisci. "Kullback–Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability." Automatica 50, no. 3 (March 2014): 707–18. http://dx.doi.org/10.1016/j.automatica.2013.11.042.
Full textHsu, Chia-Ling, and Wen-Chung Wang. "Multidimensional Computerized Adaptive Testing Using Non-Compensatory Item Response Theory Models." Applied Psychological Measurement 43, no. 6 (October 26, 2018): 464–80. http://dx.doi.org/10.1177/0146621618800280.
Full textMarsh, Patrick. "THE PROPERTIES OF KULLBACK–LEIBLER DIVERGENCE FOR THE UNIT ROOT HYPOTHESIS." Econometric Theory 25, no. 6 (December 2009): 1662–81. http://dx.doi.org/10.1017/s0266466609990284.
Full textYang, Ce, Dong Han, Weiqing Sun, and Kunpeng Tian. "Distributionally Robust Model of Energy and Reserve Dispatch Based on Kullback–Leibler Divergence." Electronics 8, no. 12 (December 1, 2019): 1454. http://dx.doi.org/10.3390/electronics8121454.
Full textMakalic, E., and D. F. Schmidt. "Fast Computation of the Kullback–Leibler Divergence and Exact Fisher Information for the First-Order Moving Average Model." IEEE Signal Processing Letters 17, no. 4 (April 2010): 391–93. http://dx.doi.org/10.1109/lsp.2009.2039659.
Full textWeijs, Steven V., and Nick van de Giesen. "Accounting for Observational Uncertainty in Forecast Verification: An Information-Theoretical View on Forecasts, Observations, and Truth." Monthly Weather Review 139, no. 7 (July 1, 2011): 2156–62. http://dx.doi.org/10.1175/2011mwr3573.1.
Full textGao, Zhang, Xiao, and Li. "Kullback–Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information." Sensors 19, no. 21 (November 3, 2019): 4783. http://dx.doi.org/10.3390/s19214783.
Full textDissertations / Theses on the topic "Kullback-leibler average"
FANTACCI, CLAUDIO. "Distributed multi-object tracking over sensor networks: a random finite set approach." Doctoral thesis, 2015. http://hdl.handle.net/2158/1003256.
Full textBook chapters on the topic "Kullback-leibler average"
"* for a replicate design account for VarD; * for example here, set VarD=0; varD=0.0; s = sqrt(varD + sigmaW*sigmaW); It is worth noting here that REML modelling in replicate designs and the resulting ABE assessments are sensitive to the way in which the variance-covariance matrix is constructed (Patterson and Jones, 2002a). The recommended FDA procedure (FDA Guidance, 2001) provides bi-ased variance estimates (Patterson and Jones, 2002c) in certain situa-tions; however, it also constrains the Type I error rate to be less than 5% for average bioequivalence due to the constraints placed on the variance-covariance parameter space, which is a desirable property for regulators reviewing such data. 7.7 Kullback–Leibler divergence Dragalin and Fedorov (1999) and Dragalin et al. (2002) pointed out some disadvantages of using the metrics for ABE, PBE and IBE, that we have described in the previous sections, and proposed a unified approach to equivalence testing based on the Kullback–Leibler divergence (KLD) (Kullback and Leibler, 1951). In this approach bioequivalence testing is regarded as evaluating the distance between two distributions of selected pharmacokinetic statistics or parameters for T and R. For example, the selected statistics might be log(AUC) or log(Cmax), as used in the previous sections. To demonstrate bioequivalence, the following hypotheses are tested: H : d(f ) > d vs. H , (7.15) where f are the appropriate density functions of the observa-tions from T and R, respectively, and d is a pre-defined boundary or goal-post. Equivalence is determined if the following null hypothesis is rejected. For convenience the upper bound of a 90% confidence interval, d . If d then bioequivalence is accepted; otherwise it is rejected. Under the assumption that T and R have the same variance, i.e., σ , the KLD for ABE becomes (µ −µ ) d( fT , f σ2 which differs from the (unscaled) measure defined in Section 4.2. If the statistics (e.g., log(AUC)) for T and R are normally distributed with means µ , respectively, and variances σ." In Design and Analysis of Cross-Over Trials, 371. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9781420036091-26.
Full textConference papers on the topic "Kullback-leibler average"
Lu, Kelin, Kuo-Chu Chang, and Rui Zhou. "Weighted Kullback-Leibler average-based distributed filtering algorithm." In SPIE Defense + Security, edited by Ivan Kadar. SPIE, 2015. http://dx.doi.org/10.1117/12.2177493.
Full textWang, Baobao, and Lianzhong Zhang. "Weight Kullback-Leibler Average Interactive multiple model Probabilistic Data Association Filter." In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). IEEE, 2020. http://dx.doi.org/10.1109/icmcce51767.2020.00269.
Full textFan, Cody, Tsang-Kai Chang, and Ankur Mehta. "Kullback-Leibler Average of von Mises Distributions in Multi-Agent Systems." In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9303876.
Full textYuMing, Du. "Evaluation Criterion of Linear Model Order Selection Approaches Based Average Kullback-Leibler Divergence." In 2009 WRI Global Congress on Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/gcis.2009.340.
Full textKatariya, Sumeet, Branislav Kveton, Csaba Szepesvári, Claire Vernade, and Zheng Wen. "Bernoulli Rank-1 Bandits for Click Feedback." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/278.
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