Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM
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[1] A. Haar. Der Massbegriff in der Theorie der Kontinuierlichen Gruppen , 1933 .
[2] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[3] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[4] P. Diaconis. Group representations in probability and statistics , 1988 .
[5] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[6] M. L. Eaton. Group invariance applications in statistics , 1989 .
[7] R. Wijsman. Invariant measures on groups and their use in statistics , 1990 .
[8] Peter M. Fenwick,et al. A new data structure for cumulative frequency tables , 1994, Softw. Pract. Exp..
[9] L. Tierney. Markov Chains for Exploring Posterior Distributions , 1994 .
[10] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[11] Joseph T. Chang,et al. Conditioning as disintegration , 1997 .
[12] L. Tierney. A note on Metropolis-Hastings kernels for general state spaces , 1998 .
[13] Jun S. Liu,et al. Parameter Expansion for Data Augmentation , 1999 .
[14] A. U.S.,et al. Generalised Gibbs sampler and multigrid Monte Carlo for Bayesian computation , 2000 .
[15] Hinrich Schütze,et al. Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.
[16] Sebastian Thrun,et al. Probabilistic robotics , 2002, CACM.
[17] Tim Hesterberg,et al. Monte Carlo Strategies in Scientific Computing , 2002, Technometrics.
[18] Alexander J. Smola,et al. Kernels and Regularization on Graphs , 2003, COLT.
[19] Yang Xiang,et al. PROBABILISTIC REASONING IN MULTIAGENT SYSTEMS: A GRAPHICAL MODELS APPROACH, by Yang Xiang, Cambridge University Press, Cambridge, 2002, xii + 294 pp., ISBN 0-521-81308-5 (Hardback, £45.00). , 2002, Robotica.
[20] J. Rosenthal,et al. General state space Markov chains and MCMC algorithms , 2004, math/0404033.
[21] Daniel Zelterman,et al. Bayesian Artificial Intelligence , 2005, Technometrics.
[22] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[23] Tony Jebara,et al. Multi-object tracking with representations of the symmetric group , 2007, AISTATS.
[24] I. Kondor,et al. Group theoretical methods in machine learning , 2008 .
[25] Adnan Darwiche,et al. Modeling and Reasoning with Bayesian Networks , 2009 .
[26] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[27] Joseph A. Djugash,et al. Geolocation with Range: Robustness, Efficiency and Scalability , 2010 .
[28] András György,et al. A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping , 2010, AISTATS.
[29] Alan K. Mackworth,et al. Artificial Intelligence - Foundations of Computational Agents , 2010 .
[30] L Poole David,et al. Artificial Intelligence: Foundations of Computational Agents , 2010 .
[31] Walter Dempsey,et al. Multiresolution analysis on the symmetric group , 2012, NIPS.
[32] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[33] Simon J. D. Prince,et al. Computer Vision: Models, Learning, and Inference , 2012 .
[34] Mathias Niepert,et al. Markov Chains on Orbits of Permutation Groups , 2012, UAI.
[35] Mathias Niepert. Lifted Probabilistic Inference: An MCMC Perspective , 2012, StarAI@UAI.
[36] Byron Boots,et al. A Spectral Learning Approach to Range-Only SLAM , 2012, ICML.
[37] Jorge Dias,et al. Probabilistic Approaches to Robotic Perception , 2014, Springer Tracts in Advanced Robotics.
[38] Maurizio Dapor. Monte Carlo Strategies , 2020, Transport of Energetic Electrons in Solids.