TA4a.1

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
Session:
TA4a: State Estimation and Bayesian Inference Lecture
Track:
Adaptive Systems, Machine Learning, and Data Analytics
Location:
Nautilus
Presentation Time:
Tue, 28 Oct, 08:15 - 08:40 PT (UTC -8)
Session Chair:
Jesse Friedbaum, University of Arizona
Presentation
Discussion
Resources
No resources available.
Session TA4a
TA4a.1: 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
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
TA4a.3: Uncertainty Quantification and Posterior Estimation via Pair-Wise Contrast
Jesse Friedbaum, Sudarshan Adiga, Ravi Tandon, University of Arizona, United States
TA4a.4: Finding a Path from 1-Bit Quantizer: State Estimation with 1-Bit Observations and Imperfect Models
Chaehyun Jung, Jeonghun Park, Yonsei University, Republic of Korea
Contacts