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Approximate Bayesian inference in spatial environments [article]

Atanas Mirchev, Baris Kayalibay, Maximilian Soelch, Patrick van der Smagt, Justin Bayer
2019 arXiv   pre-print
In the context of spatial environments, different types of problems such as localisation, mapping, navigation or autonomous exploration are typically adressed with specialised methods, often relying on  ...  The method performs comparably to specialised state-of-the-art methodology in two distinct simulated environments.  ...  Approximation via variational inference Inference in models such as eq. (1) is intractable in most cases.  ... 
arXiv:1805.07206v3 fatcat:ninf4uedszbtnpmlmkh4krenmy

Bayesian Integration of Information in Hippocampal Place Cells

Tamas Madl, Stan Franklin, Ke Chen, Daniela Montaldi, Robert Trappl, Gareth Robert Barnes
2014 PLoS ONE  
In this paper, we propose that Hippocampal place cells might implement such an error correction mechanism by integrating different sources of information in an approximately Bayes-optimal fashion.  ...  Accurate spatial localization requires a mechanism that corrects for errors, which might arise from inaccurate sensory information or neuronal noise.  ...  of approximate inference.  ... 
doi:10.1371/journal.pone.0089762 pmid:24603429 pmcid:PMC3945610 fatcat:5svs2qc4g5at7f2kk2sbxuz3ye

Practical Bayesian modeling and inference for massive spatial data sets on modest computing environments †

Lu Zhang, Abhirup Datta, Sudipto Banerjee
2019 Statistical analysis and data mining  
This article devises massively scalable Bayesian approaches that can rapidly deliver inference on spatial process that are practically indistinguishable from inference obtained using more expensive alternatives  ...  Scalable spatial process models have been found especially attractive due to their richness and flexibility and, particularly so in the Bayesian paradigm, due to their presence in hierarchical model settings  ...  Aki Vehtari of the STAN Development Team for useful guidance regarding the implementation of non-conjugate NNGP models in Stan for full Bayesian inference.  ... 
doi:10.1002/sam.11413 pmid:33868538 pmcid:PMC8048149 fatcat:idiq6avwpbhzzmsxib5uxkstbe

A Primer on the Bayesian Approach to High-Density Single-Molecule Trajectories Analysis

Mohamed El Beheiry, Silvan Türkcan, Maximilian U. Richly, Antoine Triller, Antigone Alexandrou, Maxime Dahan, Jean-Baptiste Masson
2016 Biophysical Journal  
In this primer, we present a Bayesian approach toward treating these data, and we discuss how it can be fruitfully employed to infer physical and biochemical parameters from single-molecule trajectories  ...  Tracking single molecules in living cells provides invaluable information on their environment and on the interactions that underlie their motion.  ...  In this primer, we discuss the use of Bayesian inference methods to analyze single-molecule trajectories. First, we recall the basic principles of Bayesian inference.  ... 
doi:10.1016/j.bpj.2016.01.018 pmid:27028631 pmcid:PMC4816684 fatcat:qhvnto77xree3o3wyofym7sd34

A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation

Scott W. Linderman, Matthew J. Johnson, Matthew A. Wilson, Zhe Chen
2016 Journal of Neuroscience Methods  
Results-The effectiveness of our Bayesian approaches is demonstrated on recordings from a freely-behaving rat navigating in an open field environment.  ...  New method-We extend our previous work and propose a novel nonparametric Bayesian approach to infer rat hippocampal population codes during spatial navigation.  ...  Spatial representation of the environment is pivotal for navigation in rodents (O'Keefe and Nadel, 1978) .  ... 
doi:10.1016/j.jneumeth.2016.01.022 pmid:26854398 pmcid:PMC4801699 fatcat:aqgqsrkgzjg2rlc2ygwtl7h4eu

A computational cognitive framework of spatial memory in brains and robots

Tamas Madl, Stan Franklin, Ke Chen, Robert Trappl
2018 Cognitive Systems Research  
Here, we describe a computational framework for robotic architectures aiming to function in realistic environments, as well as to be cognitively plausible.  ...  We motivate and describe several mechanisms towards achieving this despite the sensory noise and spatial complexity inherent in the physical world.  ...  approximately optimal inference .  ... 
doi:10.1016/j.cogsys.2017.08.002 fatcat:ubohygvimnd4xbjsjpyeoxzx5m

Page 357 of Genetics Vol. 177, Issue 1 [page]

2007 Genetics  
EMERSON, 2007 Assessing the affect of genetic mutation: a Bayesian framework for deter- mining population history from DNA sequence data, pp. 25- 50 in Bayesian Statistics 8, edited by J. M.  ...  PyBus, 2005 Bayesian coalescent inference of past population dynam- ics from molecular sequences. Mol. Biol. Evol. 22: 1185-1192. Epwarps, S. V., L. Liu and D. K.  ... 

A fast Bayesian model for latent radio signal prediction

B. Houlding, A. Bhattacharya, S.P. Wilson, T.K. Forde
2009 2009 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks  
Index Terms-Dynamic spectrum access, latent radio signal, Bayesian estimation, integrated nested Laplace approximation.  ...  This paper considers the use of a recently developed Bayesian statistical approximation technique that leads to very fast determination of highly accurate estimates for latent radio signal power.  ...  to changes in the radio environment.  ... 
doi:10.1109/wiopt.2009.5291568 dblp:conf/wiopt/HouldingBWF09 fatcat:xro65u5nsre3tjfh72pqwecb2e

A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation [article]

Scott W. Linderman, Matthew J. Johnson, Matthew A. Wilson, Zhe Chen
2014 arXiv   pre-print
We demonstrate the effectiveness of our Bayesian approaches on recordings from a freely-behaving rat navigating in an open field environment.  ...  Here we extend our previous work and propose a nonparametric Bayesian approach to infer rat hippocampal population codes during spatial navigation.  ...  Spatial representation of the environment is pivotal for navigation in rodents (O'Keefe and Nadel, 1978) .  ... 
arXiv:1411.7706v1 fatcat:6sm4225smfajtofntnf566bgue

Gaussian Markov Random Fields for fusion in information form

Liye Sun, Teresa Vidal-Calleja, Jaime Valls Miro
2016 2016 IEEE International Conference on Robotics and Automation (ICRA)  
2.5D maps are preferable for representing the environment owing to their compactness.  ...  The proposed approach allows accurate estimation of 2.5D maps at arbitrary resolution, while incorporating sensor noise and spatial dependency in a statistically sound way.  ...  INTRODUCTION Two-and-a-half dimensional (2.5D) maps have been widely used in robotics to represent the environment in a compact manner.  ... 
doi:10.1109/icra.2016.7487329 dblp:conf/icra/SunVM16 fatcat:wrx2bi5f2zc7dlep2my75ufxue

Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model [article]

Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi, Tetsunari Inamura
2020 arXiv   pre-print
Robots are required to not only learn spatial concepts autonomously but also utilize such knowledge for various tasks in a domestic environment.  ...  We demonstrated path planning based on human instruction using acquired spatial concepts to verify the usefulness of the proposed approach in the simulator and in real environments.  ...  The authors also thank Kazuya Asada and Keishiro Taguchi for providing virtual home environments and training datasets for spatial concepts in SIGVerse simulator.  ... 
arXiv:2002.07381v2 fatcat:c3wrjqe57zdmbga2ht3myi2p3m

Spatial bayesian learning algorithms for geographic information retrieval

Arron R. Walker, Binh Pham, Miles Moody
2005 Proceedings of the 2005 international workshop on Geographic information systems - GIS '05  
The next step in using Bayesian Inference for GIR is to develop algorithms to automatically create a Bayesian network from historical data.  ...  The use of Bayesian inference for GIR relies on a manually created Bayesian network. The Bayesian network contains causal probability relationships between spatial themes.  ...  Bayesian Inference will be explained in more detail later in this paper.  ... 
doi:10.1145/1097064.1097080 dblp:conf/gis/WalkerPM05 fatcat:xbkvzayhp5fqnegtmuo5qy5cfe

Page 1519 of Genetics Vol. 181, Issue 4 [page]

2009 Genetics  
ANDOLFATTO, 2006 Approximate Bayesian inference reveals evidence for a recent, severe bottleneck in a Netherlands population of Drosophila melanogaster. Genetics 172: 1607-1619.  ...  ., 2008 Colonization history of ‘the Swiss Rhine Basin by the bullhead (Cottus gobio): inference under a Bayesian spatially explicit framework. Mol. Ecol. 17: 757-772. NIELSEN, R., S. WILLIAMSON, Y.  ... 

Spatial statistics for environmental studies

Alessandro Fassò, Alessio Pollice, Barbara Cafarelli
2013 AStA Advances in Statistical Analysis  
The paper of Cameletti et al. (2012) reworks a known air quality modeling exercise using the recently introduced approach to Bayesian inference known as INLA.  ...  catchment scale and, last but not least, one is concerned with radioactive contamination of submarine environment.  ... 
doi:10.1007/s10182-013-0209-x fatcat:5z5meg7n6zgt5jw3zygmtvytqy

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

Tim Rohe, Uta Noppeney, Christoph Kayser
2015 PLoS Biology  
Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference  ...  It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition.  ...  Bayesian Causal Inference Model Details of the Bayesian Causal Inference model of audiovisual perception can be found in Koerding and colleagues [2] .  ... 
doi:10.1371/journal.pbio.1002073 pmid:25710328 pmcid:PMC4339735 fatcat:afxy4j3a2fhfjlmeevnkkupyba
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