Bayesian probability
Related papers: 20
About
Bayesian probability is a mathematical framework that interprets probability as a degree of belief, updated continuously as new evidence is observed. Rooted in Bayes' theorem, it provides a principled way to combine prior knowledge with incoming data to produce refined posterior estimates. In robotics and AI, Bayesian methods are foundational to tasks such as robot localization, where algorithms like Monte Carlo Localization and particle filters maintain probabilistic estimates of a robot's position and update them with sensor readings. They underpin Simultaneous Localization and Mapping (SLAM), occupancy grid construction, object tracking, and Bayesian optimization for tuning robot gaits or controller parameters. In machine learning, Bayesian approaches quantify uncertainty in neural network predictions, enabling systems to recognize when inputs fall outside their training distribution. This matters enormously in safety-critical applications — autonomous vehicles, medical robotics, and human-robot interaction — where knowing *how confident* a system is can be as important as the prediction itself. By formalizing uncertainty rather than ignoring it, Bayesian probability enables robots and AI systems to make robust, rational decisions in noisy, unpredictable real-world environments.
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Top Cited Papers
Using occupancy grids for mobile robot perception and navigation
Alberto Elfes
Citations: 2173 • 1989
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit
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Probabilistic machine learning and artificial intelligence
Zoubin Ghahramani
Citations: 1967 • 2015
Robust Monte Carlo localization for mobile robots
Sebastian Thrun, Dieter Fox, Wolfram Burgard, Frank Dellaert
Citations: 1803 • 2001
Monte Carlo localization for mobile robots
Frank Dellaert, D. Fox, Wolfram Burgard, Sebastian Thrun
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Mixture density networks
Chris Bishop
Citations: 1273 • 1994
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Kevin J. Murphy, Stuart Russell
Citations: 1185 • 2001
A survey of uncertainty in deep neural networks
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Monte Carlo localization: efficient position estimation for mobile robots
Dieter Fox, Wolfram Burgard, Frank Dellaert, Sebastian Thrun
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Bayesian approach to extended object and cluster tracking using random matrices
Wolfgang Koch
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Bayesian Online Changepoint Detection
Ryan P. Adams, David Mackay
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Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words
Adrien Angeli, David Filliat, Stéphane Doncieux, Jean-Arcady Meyer
Citations: 515 • 2008
Acting under uncertainty: discrete Bayesian models for mobile-robot navigation
Anthony R. Cassandra, Leslie Pack Kaelbling, James Kurien
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Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter
Wei Wu, Yun Gao, Elie Bienenstock, John P. Donoghue, Michael J. Black
Citations: 459 • 2005
Bayesian Map Learning in Dynamic Environments
Kevin P. Murphy
Citations: 451 • 1999
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
Citations: 400 • 2014
Bayesian Optimization in a Billion Dimensions via Random Embeddings
Ziyu Wang, Frank Hutter, Masrour Zoghi, David S. Matheson, Nando De Feitas
Citations: 372 • 2016
Bayesian optimization for learning gaits under uncertainty
Roberto Calandra, André Seyfarth, Jan Peters, Marc Peter Deisenroth
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Bayesian Inference and Maximum Entropy Methods in Science and Engineering
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A General Framework for Uncertainty Estimation in Deep Learning
Citations: 272 • 2020