TA4a: State Estimation and Bayesian Inference
Tue, 28 Oct, 08:15 - 09:55 PT (UTC -7)
Location: Nautilus
Session Type: Lecture
Track: Adaptive Systems, Machine Learning, and Data Analytics
Tue, 28 Oct, 08:15 - 08:40 PT (UTC -7)

TA4a.1: Finding a Path from 1-Bit Quantizer: State Estimation with 1-Bit Observations and Imperfect Models
Chaehyun Jung, Jeonghun Park, Yonsei University, Seoul, South Korea, Republic of Korea
Tue, 28 Oct, 08:40 - 09:05 PT (UTC -7)

TA4a.2: Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
Benjamin Deutschmann, Graz University of Technology, Austria; Xuhong Li, Florian Meyer, University of California San Diego, United States; Erik Leitinger, Graz University of Technology, Austria
Tue, 28 Oct, 09:05 - 09:30 PT (UTC -7)

TA4a.3: Uncertainty Quantification and Posterior Estimation via Pair-Wise Contrast
Jesse Friedbaum, Sudarshan Adiga, Ravi Tandon, The Univeristy of Arizona, United States
Tue, 28 Oct, 09:30 - 09:55 PT (UTC -7)

TA4a.4: Pollen Quantification Using Bayesian Probability Mass Function Estimation with Automatic Rank Detection
Alla Manina, Joseph Kibugi Chege, Technische Universität Ilmenau, Germany; Thomas Hornick, Susanne Dunker, Helmholtz-Centre for Environmental Research (UFZ), German Centre for Integrative Biodiversity Research (iDiv), Germany; Arie Yeredor, Tel Aviv University, Israel; Martin Haardt, Technische Universität Ilmenau, Israel