Search Results - "Gaussian Processes in Machine Learning"

The impact of human expert visual inspection on the discovery of strong gravitational lenses

Authors: Rojas, Karina Collett, Thomas E Ballard, Daniel et al.

Contributors: Rojas, Karina Collett, Thomas E Ballard, Daniel et al.

Source: Digital.CSIC. Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)
lenseuclid 2023, 'The impact of human expert visual inspection on the discovery of strong gravitational lenses', MNRAS, vol. 523, no. 3, pp. 4413-4430. https://doi.org/10.1093/mnras/stad1680
MNRAS

Subject Terms: Artificial intelligence, Aérospatiale, astronomie & astrophysique, Cosmology and Nongalactic Astrophysics (astro-ph.CO), Gravitational lensing: strong, Physique, chimie, mathématiques & sciences de la terre, FOS: Physical sciences, Astrophysics, 01 natural sciences, [SDU] Sciences of the Universe [physics], Gaussian Processes in Machine Learning, Deep Learning, Physical, chemical, mathematical & earth Sciences, Artificial Intelligence, Dark energy, 0103 physical sciences, FOS: Mathematics, Lens (geology), Gaze, Galaxy Formation and Evolution in the Universe, Physics, Confidence interval, Statistics, gravitational lensing: strong, Astronomy and Astrophysics, Optics, Redshift, Astrophysics - Astrophysics of Galaxies, Computer science, Gamma-Ray Bursts and Supernovae Connections, Cosmology, Galaxy, Physics and Astronomy, Astrophysics of Galaxies (astro-ph.GA), Physical Sciences, Computer Science, Space science, astronomy & astrophysics, Classifier (UML), Gravitational lens, Mathematics, Astrophysics - Cosmology and Nongalactic Astrophysics

File Description: application/pdf

Access URL: http://arxiv.org/abs/2301.03670
http://hdl.handle.net/10261/350282
https://hdl.handle.net/2268/332853
https://doi.org/10.1093/mnras/stad1680
https://insu.hal.science/insu-04473164v1/document
https://doi.org/10.1093/mnras/stad1680
https://insu.hal.science/insu-04473164v1
https://academic.oup.com/mnras/article/523/3/4413/7191857
https://hdl.handle.net/11585/962614
https://doi.org/10.1093/mnras/stad1680

Optimal Subsampling Bootstrap for Massive Data

Authors: Yingying Ma Chenlei Leng Hansheng Wang

Source: Journal of Business & Economic Statistics. 42:174-186

Subject Terms: FOS: Computer and information sciences, Statistics and Probability, Resampling, Artificial intelligence, Learning and Inference in Bayesian Networks, Regularization and Variable Selection Methods, Nonparametric Methods, Estimator, 01 natural sciences, Methodology (stat.ME), Gaussian Processes in Machine Learning, FOS: Economics and business, Inference, Artificial Intelligence, Machine learning, FOS: Mathematics, Econometrics, 0101 mathematics, Data mining, Statistics - Methodology, Model Selection, Hyperparameter, Statistics, Measure (data warehouse), Computer science, Bootstrapping (finance), Variable Selection, Physical Sciences, Computer Science, Mathematics, Statistical inference

Access URL: http://arxiv.org/abs/2302.07533

Trivariate Stochastic Weather Model for Predicting Maize Yield

Authors: Patrick Chidzalo Phillip O. Ngare Joseph Mungatu

Source: Journal of Applied Mathematics, Vol 2022 (2022)

Subject Terms: Interdependence, Adaptation to Climate Change in Agriculture, FOS: Political science, FOS: Law, Plant Science, Yield (engineering), 01 natural sciences, Agricultural and Biological Sciences, Gaussian Processes in Machine Learning, FOS: Economics and business, Artificial Intelligence, QA1-939, FOS: Mathematics, Climate change, Econometrics, 0101 mathematics, Biology, Political science, Ecology, Evolution, Behavior and Systematics, 2. Zero hunger, Evapotranspiration, Ecology, Physics, Statistics, Life Sciences, 15. Life on land, Dynamic Modeling of Plant Form and Growth, 13. Climate action, FOS: Biological sciences, Computer Science, Physical Sciences, Maize Yield, Thermodynamics, Law, Mathematics, Forecasting

File Description: text/xhtml

Access URL: https://doaj.org/article/7e3da2850acc45e191f59a96fb19694c

Artificial Intelligence Approaches for Studying the pp Interactions at High Energy Using Adaptive Neuro-Fuzzy Interface System

Authors: D. M. Habashy M. Y. El-Bakry El‐Sayed A. El‐Dahshan et al.

Source: Artificial Intelligence ISBN: 9781803561431

Subject Terms: Artificial neural network, Artificial intelligence, Inference system, Statistics, Paleontology, Statistical and Nonlinear Physics, Variational Inference, Adaptive neuro fuzzy inference system, Computer science, Estimator, Gaussian Processes in Machine Learning, Fuzzy logic, Neuro-fuzzy, Context (archaeology), Physics and Astronomy, Fuzzy control system, Artificial Intelligence, Statistical Mechanics with Long-Range Interactions and Nonextensivity, Computer Science, Physical Sciences, Machine learning, FOS: Mathematics, Scientific Computing and Data Analysis with Python, Biology, Mathematics

Access URL: https://mts.intechopen.com/articles/show/title/artificial-intelligence-approaches-for-studying-the-pp-interactions-at-high-energy-using-adaptive-ne

Dark Energy Survey Year 3 results: marginalization over redshift distribution uncertainties using ranking of discrete realizations

Authors: J. Cordero I. Harrison R. P. Rollins et al.

Contributors: J. Cordero I. Harrison R. P. Rollins et al.

Source: Biblos-e Archivo. Repositorio Institucional de la UAM
Universidad Autónoma de Madrid
Digital.CSIC. Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)
instname
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Cordero, J P, Harrison, I, Rollins, R P, Bernstein, G M, Bridle, S L, Alarcon, A, Alves, O, Amon, A, Andrade-oliveira, F, Camacho, H, Campos, A, Choi, A, Derose, J, Dodelson, S, Eckert, K, Eifler, T F, Everett, S, Fang, X, Friedrich, O, Gruen, D, Gruendl, R A, Hartley, W G, Huff, E M, Krause, E, Kuropatkin, N, Maccrann, N, Mccullough, J, Myles, J, Pandey, S, Raveri, M, Rosenfeld, R, Rykoff, E S, Sánchez, C, Sánchez, J, Sevilla-noarbe, I, Sheldon, E, Troxel, M, Wechsler, R, Yanny, B, Yin, B, Zhang, Y, Aguena, M, Allam, S, Bertin, E, Brooks, D, Burke, D L, Carnero rosell, A, Carrasco kind, M, Carretero, J, Castander, F J, Cawthon, R, Costanzi, M, Da costa, L, Da silva pereira, M E, De vicente, J, Diehl, H T, Dietrich, J, Doel, P, Elvin-poole, J, Ferrero, I, Flaugher, B, Fosalba, P, Frieman, J, Garcia-bellido, J, Gerdes, D, Gschwend, J, Gutierrez, G, Hinton, S, Hollowood, D L, Honscheid, K, Hoyle, B, James, D, Kuehn, K, Lahav, O, Maia, M A G, March, M, Menanteau, F, Miquel, R, Morgan, R, Muir, J, Palmese, A, Paz-chinchon, F, Pieres, A, Plazas malagón, A, Sánchez, E, Scarpine, V, Serrano, S, Smith, M, Soares-santos, M, Suchyta, E, Swanson, M, Tarle, G, Thomas, D, To, C & Varga, T N 2022, 'Dark Energy Survey Year 3 results: marginalization over redshift distribution uncertainties using ranking of discrete realizations', Monthly Notices of the Royal Astronomical Society, vol. 511, no. 2, pp. 2170-2185. https://doi.org/10.1093/mnras/stac147

Subject Terms: Large-Scale Structure of Universe, Cosmology and Nongalactic Astrophysics (astro-ph.CO), Large-scale structure of Universe, astronomi: 438, Galaxies: distances and redshifts, Gravitational lensing: weak, Methods: numerical, FOS: Physical sciences, Nonparametric Methods, Astrophysics, 01 natural sciences, methods: numerical, [SDU] Sciences of the Universe [physics], Gaussian Processes in Machine Learning, gravitational lensing: weak, Galaxies: Distances and Redshifts, Artificial Intelligence, Sparse Regression, Large-scale structure of the universe, Dark energy, 0103 physical sciences, Galaxies: distances and redshift, Distances and Redshifts [Galaxies], Cosmological Parameters and Dark Energy, Weak gravitational lensing, Numerical [Methods], Gravitational Lensing: Weak, Galaxy Formation and Evolution in the Universe, Physics, Física, Astronomy and Astrophysics, Redshift, Cosmology, Galaxy, Physics and Astronomy, Redshift survey, Methods: Numerical, Weak [Gravitational Lensing], Physical Sciences, Computer Science, Photometric redshift, VDP::Astrofysikk, large-scale structure of Universe, Statistical physics, galaxies: distances and redshifts, Astrophysics - Cosmology and Nongalactic Astrophysics

File Description: application/pdf

Access URL: http://arxiv.org/pdf/2109.09636
http://arxiv.org/abs/2109.09636
http://hdl.handle.net/10486/704346
http://hdl.handle.net/10261/296686
https://research.manchester.ac.uk/en/publications/f9111ac2-2e61-4db5-a12d-63f8d05bbe60
https://doi.org/10.1093/mnras/stac147
https://hdl.handle.net/11368/3015198
https://academic.oup.com/mnras/article/511/2/2170/6516434
http://hdl.handle.net/10852/94771
https://doi.org/10.1093/mnras/stac147
https://insu.hal.science/insu-03748279v1
https://insu.hal.science/insu-03748279v1/document
https://doi.org/10.1093/mnras/stac147
https://hdl.handle.net/11368/3015198
https://academic.oup.com/mnras/article/511/2/2170/6516434
https://doi.org/10.1093/mnras/stac147
https://hdl.handle.net/11567/1104520
https://doi.org/10.1093/mnras/stac147
https://discovery-pp.ucl.ac.uk/id/eprint/10145123/

Estimation of the Conditional Probability Using a Stochastic Gradient Process

Authors: Ali Labriji Abdelkrim Bennar Mostafa Rachik

Source: Journal of Mathematics, Vol 2021 (2021)

Subject Terms: Statistics and Probability, 0301 basic medicine, Volume (thermodynamics), Economics, Conditional probability, Geometry, Regularization and Variable Selection Methods, Social psychology, Quantum mechanics, Gaussian Processes in Machine Learning, FOS: Economics and business, 03 medical and health sciences, Point (geometry), Artificial Intelligence, QA1-939, FOS: Mathematics, Psychology, Econometrics, Active Learning in Machine Learning Research, Data mining, Probabilistic Models, Point process, Economic growth, Conditional expectation, 0303 health sciences, Physics, Mathematical optimization, Statistics, Computer science, Process (computing), Management, Algorithm, FOS: Psychology, Popularity, Operating system, Computer Science, Physical Sciences, Convergence (economics), Covariance Estimation, Estimation, Mathematics

File Description: text/xhtml

Access URL: https://downloads.hindawi.com/journals/jmath/2021/7660113.pdf
https://doaj.org/article/7fc7d8904f0c48e6a85226bca384a9d2

Adaptive probability hypothesis density filter for multi-target tracking with unknown measurement noise statistics

Authors: Wenyuan Xu

Source: Measurement + Control, Vol 54 (2021)

Subject Terms: Adaptive filter, Artificial intelligence, Outlier Detection, Noise measurement, Particle Filtering and Nonlinear Estimation Methods, Noise (video), 02 engineering and technology, Nonparametric Methods, Gaussian noise, Quantum mechanics, Filter (signal processing), Gaussian Processes in Machine Learning, 0203 mechanical engineering, Artificial Intelligence, FOS: Mathematics, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, T1-995, Noise reduction, Technology (General), Control engineering systems. Automatic machinery (General), Covariance, Physics, Statistics, Computer science, Algorithm, Gaussian Filters, TJ212-225, Computer Science, Physical Sciences, Gaussian, Computer vision, Multitarget Tracking, Model-Based Clustering with Mixture Models, Mathematics

Access URL: https://journals.sagepub.com/doi/pdf/10.1177/0020294021992800
https://doaj.org/article/88ed5aa216b64febb1446d3c4254dbd5
https://journals.sagepub.com/doi/full/10.1177/0020294021992800
https://journals.sagepub.com/doi/pdf/10.1177/0020294021992800
https://doaj.org/article/88ed5aa216b64febb1446d3c4254dbd5

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

Authors: Keith Bechtol M. Carrasco Kind E. J. Sanchez et al.

Contributors: Keith Bechtol M. Carrasco Kind E. J. Sanchez et al.

Source: Digital.CSIC. Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)
instname

Subject Terms: FOS: Computer and information sciences, statistical [Methods], Computer Science - Machine Learning, Artificial intelligence, software: data analysis, Astrophysics, 01 natural sciences, Machine Learning (cs.LG), Gaussian Processes in Machine Learning, Probability distribution, Software: data analysis, Sparse Regression, Probability density function, Astrophysics - Cosmology and Nongalactic Astrophysic, data analysis [Methods], galaxies: fundamental parameter, Methods: statistical, Ecology, Galaxy Formation and Evolution in the Universe, Physics, Star formation, Python (programming language), Statistics, Galaxies: evolution, galaxies: fundamental parameters, Remote Sensing in Vegetation Monitoring and Phenology, Cosmology, Algorithm, Software: public realese, fundamental parameters [Galaxies], Physical Sciences, Photometric redshift, software: data analysi, galaxies: evolution, Astrophysics - Instrumentation and Methods for Astrophysics, methods: data analysi, Astrophysics - Cosmology and Nongalactic Astrophysics, public realese [Software], Galaxies: fundamental parameters, Cosmology and Nongalactic Astrophysics (astro-ph.CO), methods: data analysis, methods: statistical, software: public release, Astrophysics - Astrophysics of Galaxies, FOS: Physical sciences, [INFO] Computer Science [cs], Joint probability distribution, Astrophysics - Astrophysics of Galaxie, Methods: data analysis, Artificial Intelligence, Stellar mass, 0103 physical sciences, FOS: Mathematics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Astronomy and Astrophysics, Redshift, evolution [Galaxies], Computer science, Stars, Galaxy, Operating system, Physics and Astronomy, [PHYS.PHYS.PHYS-INS-DET] Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], data analysis [Software], Photometry (optics), Astrophysics of Galaxies (astro-ph.GA), FOS: Biological sciences, Environmental Science, Computer Science, [PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph], Astrophysics - Instrumentation and Methods for Astrophysic, Mathematics, Random forest

File Description: application/pdf; PDF

Access URL: https://academic.oup.com/mnras/article-pdf/502/2/2770/38831474/stab164.pdf
http://arxiv.org/abs/2012.05928
http://hdl.handle.net/10261/262803
https://academic.oup.com/mnras/article/502/2/2770/6105325#266924321
http://hdl.handle.net/11368/2988341
https://arxiv.org/abs/2012.05928
https://academic.oup.com/mnras/article/502/2/2770/6105325
https://dblp.uni-trier.de/db/journals/corr/corr2012.html#abs-2012-05928
https://www.researchwithnj.com/en/publications/a-machine-learning-approach-to-galaxy-properties-joint-redshift-s
https://arxiv.org/pdf/2012.05928.pdf
https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3340306
http://hdl.handle.net/10852/89328
https://doi.org/10.1093/mnras/stab164
https://hal.science/hal-03122291v1
https://hal.science/hal-03122291v1/document
https://doi.org/10.1093/mnras/stab164
https://academic.oup.com/mnras/article/502/2/2770/6105325#266924321
https://doi.org/10.1093/mnras/stab164
https://hdl.handle.net/11368/2988341
https://discovery-pp.ucl.ac.uk/id/eprint/10129194/

Elicitation of the Parameters of Multiple Linear Models

Authors: Carlos Barrera-Causil Juan Carlos Correa

Source: Revista Colombiana de Estadística, Volume: 44, Issue: 1, Pages: 159-170, Published: 27 FEB 2021

Subject Terms: Artificial intelligence, Conjugate prior, Learning and Inference in Bayesian Networks, Construct (python library), Linear model, Social Sciences, Expert Judgment, 02 engineering and technology, Management Science and Operations Research, Bayesian statistics, Informative distribution, Nonparametric Methods, Bayesian probability, 01 natural sciences, Elicitación, Decision Sciences, Gaussian Processes in Machine Learning, Variance (accounting), Artificial Intelligence, Prior probability, Accounting, Machine learning, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Business, 0101 mathematics, Estadística Bayesiana, Data mining, Probabilistic Models, Probabilistic Graphical Models, Probabilistic Learning, Statistics, Elicitation, Conjugate distribution, Computer science, Distribución informativa, Process (computing), Programming language, Operating system, Computer Science, Physical Sciences, Time Series Forecasting Methods, Distribución conjugada, Mathematics

File Description: text/html

Access URL: https://rcb.unal.edu.co/index.php/estad/article/view/83525
https://revistas.unal.edu.co/index.php/estad/article/view/83525
http://www.scielo.org.co/pdf/rce/v44n1/0120-1751-rce-44-01-159.pdf
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-17512021000100159
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-17512021000100159&lng=en&tlng=en

Functional mixtures-of-experts

Authors: Faïcel Chamroukhi Nhat Thien Pham Antoine Channarond et al.

Contributors: Faïcel Chamroukhi Nhat Thien Pham Antoine Channarond et al.

Source: Statistics and Computing. 34

Subject Terms: Statistics and Probability, FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Mixture Models, Generalized linear model, G.3, Machine Learning (stat.ML), 02 engineering and technology, Regularization and Variable Selection Methods, Statistics - Computation, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), Gaussian Processes in Machine Learning, Methodology (stat.ME), Cluster analysis, Context (archaeology), [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics - Machine Learning, Artificial Intelligence, Lasso (programming language), Machine learning, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Expectation–maximization algorithm, Statistics and Probability [physics.data-an], 0101 mathematics, Data mining, Biology, Statistics - Methodology, Computation (stat.CO), Statistics, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Paleontology, Computer science, Algorithm, World Wide Web, 62-XX, 62R10, Physical Sciences, Computer Science, Model-Based Clustering with Mixture Models, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Mathematics, Maximum likelihood

Access URL: http://arxiv.org/abs/2202.02249

Constraining cosmological parameters from N-body simulations with variational Bayesian neural networks

Authors: Héctor J. Hortúa Luz Ángela García Leonardo Castañeda C.

Source: Frontiers in Astronomy and Space Sciences, Vol 10 (2023)

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Astronomy, Geophysics. Cosmic physics, Bayesian inference, Nonparametric Methods, 01 natural sciences, Estimator, Machine Learning (cs.LG), Gaussian Processes in Machine Learning, Machine Learning, N-body simulations, Galaxy Formation and Evolution in the Universe, Physics, Statistics, artificial intelligence, Posterior probability, Time Series Modelling, Algorithm, deep neural networks, Physical Sciences, parameter estimation, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Artificial neural network, Cosmology and Nongalactic Astrophysics (astro-ph.CO), Computer Science - Artificial Intelligence, Flexibility (engineering), FOS: Physical sciences, QB1-991, Variational Inference, Bayesian probability, Mathematical analysis, Deep Learning, Artificial Intelligence, 0103 physical sciences, FOS: Mathematics, Instrumentation and Methods for Astrophysics (astro-ph.IM), QC801-809, Distribution (mathematics), Astronomy and Astrophysics, Applied mathematics, Computer science, Gamma-Ray Bursts and Supernovae Connections, Artificial Intelligence (cs.AI), Physics and Astronomy, Computer Science, Statistical physics, cosmology, Mathematics

Access URL: http://arxiv.org/abs/2301.03991
https://doaj.org/article/52259be129f447f5bf1ea74bb4fe25dd

Low-Rank Covariance Function Estimation for Multidimensional Functional Data

Authors: Jiayi Wang Raymond Chi-Wing Wong Xiaoke Zhang

Source: Journal of the American Statistical Association. 117:809-822

Subject Terms: FOS: Computer and information sciences, Statistics and Probability, Particle Filtering and Nonlinear Estimation Methods, Evolutionary biology, Regularization and Variable Selection Methods, Covariance function, 01 natural sciences, Systems engineering, Methodology (stat.ME), Gaussian Processes in Machine Learning, Engineering, Artificial Intelligence, FOS: Mathematics, Multivariate Approximation, 0101 mathematics, Biology, Statistics - Methodology, Covariance, Statistics, Computer science, Rank (graph theory), Function (biology), Combinatorics, Physical Sciences, Computer Science, Nonnegative Tensor Factorization, Estimation, Mathematics

Access URL: http://arxiv.org/pdf/2008.12919
http://arxiv.org/abs/2008.12919
https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1820344

Semi-parametric estimation of the variogram scale parameter of a Gaussian process with stationary increments

Authors: Azaïs, Jean-Marc Bachoc, François Lagnoux, Agnès et al.

Contributors: Azaïs, Jean-Marc Bachoc, François Lagnoux, Agnès et al.

Source: ESAIM: Probability and Statistics. 24:842-882

Subject Terms: Scale (ratio), asymptotic normality, Gaussian processes, Parametric model, [MATH] Mathematics [math], Regularization and Variable Selection Methods, Sample size determination, Digital Soil Mapping Techniques, Nonparametric Methods, Estimator, 01 natural sciences, Gaussian Processes in Machine Learning, quadratic variations, aggregation of estimators, Minimax, Classical mechanics, [MATH]Mathematics [math], Gaussian Processes, Physics, Statistics, Mathematical optimization, 16. Peace & justice, Smoothness, Kriging, Physical Sciences, Gaussian, moment method, Covariance Estimation, Statistics and Probability, Environmental Engineering, Variational Inference, Mathematical analysis, Quantum mechanics, Artificial Intelligence, FOS: Mathematics, Variogram, 0101 mathematics, minimax upper bounds, Gaussian process, FOS: Environmental engineering, Delta method, Moment (physics), Applied mathematics, scale covariance parameter, semi-parametric estimation, Parametric statistics, Computer Science, Environmental Science, Mathematics

File Description: application/pdf

Access URL: https://www.esaim-ps.org/articles/ps/pdf/2020/01/ps190095.pdf
https://www.esaim-ps.org/articles/ps/abs/2020/01/ps190095/ps190095.html
https://www.esaim-ps.org/articles/ps/pdf/2020/01/ps190095.pdf
https://hal.science/hal-03022877v1/document
https://hal.science/hal-03022877v1
https://doi.org/10.1051/ps/2020021

Sequential Monte Carlo with kernel embedded mappings: The mapping particle filter

Authors: Manuel Pulido Peter Jan van Leeuwen

Source: CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas

Subject Terms: Atmospheric Science, Resampling, Importance sampling, Artificial intelligence, Particle Filtering and Nonlinear Estimation Methods, Filter (signal processing), Gaussian Processes in Machine Learning, SEQUENTIAL BAYES, Artificial Intelligence, Particle filter, FOS: Mathematics, Genetics, KERNEL EMBEDDING, Divergence (linguistics), Sequential Monte Carlo, Biology, PARTICLE FLOWS, Mathematical optimization, Statistics, Linguistics, Hybrid Monte Carlo, Numerical Weather Prediction Models, SWAM OPTIMIZATION, Applied mathematics, Computer science, Extended Kalman filter, FOS: Philosophy, ethics and religion, Earth and Planetary Sciences, Monte Carlo method, Algorithm, Markov chain Monte Carlo, Philosophy, Combinatorics, FOS: Biological sciences, Computer Science, Physical Sciences, STEIN GRADIENT DESCENT, Kernel (algebra), Ensemble Kalman filter, FOS: Languages and literature, Computer vision, Kalman filter, OPTIMAL TRANSPORT, Mathematics, Sequence (biology), Quasi-Monte Carlo method

File Description: application/pdf

Access URL: https://www.sciencedirect.com/science/article/pii/S0021999119304681
https://dblp.uni-trier.de/db/journals/jcphy/jcphy396.html#PulidoL19
https://centaur.reading.ac.uk/85764/
http://ri.conicet.gov.ar/bitstream/11336/105970/5/CONICET_Digital_Nro.91e78343-9b2a-4d79-9e37-76d217f2100a_B.pdf
https://ri.conicet.gov.ar/handle/11336/105970
https://ui.adsabs.harvard.edu/abs/2019JCoPh.396..400P/abstract
http://hdl.handle.net/11336/105970

Learning with density matrices and random features

Authors: Fabio A. González Alejandro Gallego Santiago Toledo-Cortés et al.

Source: Quantum Machine Intelligence. 4

Subject Terms: FOS: Computer and information sciences, Backpropagation Learning, Computer Science - Machine Learning, Density matrix, Artificial intelligence, Computer Science - Artificial Intelligence, FOS: Physical sciences, Geometry, Quantum mechanics, Quantum, Machine Learning (cs.LG), Gaussian Processes in Machine Learning, Differentiable function, Artificial Intelligence, Statistical Mechanics with Long-Range Interactions and Nonextensivity, Machine learning, FOS: Mathematics, Linear algebra, Eigenvalues and eigenvectors, Quantum Physics, Geography, Physics, Pure mathematics, Statistical and Nonlinear Physics, Random matrix, Neural Network Fundamentals and Applications, Computer science, Algorithm, Artificial Intelligence (cs.AI), Physics and Astronomy, Computer Science, Physical Sciences, Benchmark (surveying), Quantum Physics (quant-ph), Mathematics, Geodesy

Access URL: http://arxiv.org/abs/2102.04394

PROVABGS: The Probabilistic Stellar Mass Function of the BGS One-percent Survey

Authors: ChangHoon Hahn Jessica Nicole Aguilar Shadab Alam et al.

Contributors: ChangHoon Hahn Jessica Nicole Aguilar Shadab Alam et al.

Source: Digital.CSIC. Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)
The Astrophysical Journal, Vol 963, Iss 1, p 56 (2024)

Subject Terms: Statistics and Probability, Stellar Populations, Cosmology and Nongalactic Astrophysics (astro-ph.CO), Astronomy, Population, FOS: Physical sciences, Stellar population, Astrophysics, 01 natural sciences, Gaussian Processes in Machine Learning, Astrostatistics, Sociology, Artificial Intelligence, Large-scale structure of the universe, 0103 physical sciences, Stellar mass, FOS: Mathematics, Galactic and extragalactic astronomy, Galaxy formation and evolution, Demography, Galaxy Formation and Evolution in the Universe, Physics, Star formation, Astronomy and Astrophysics, Initial mass function, Redshift, Galaxies, Astrophysics - Astrophysics of Galaxies, Stars, Cosmology, FOS: Sociology, QB460-466, Galaxy, Physics and Astronomy, Galaxy spectroscopy, Photometry (optics), Astrophysics of Galaxies (astro-ph.GA), Physical Sciences, Computer Science, Mathematics, Astrophysics - Cosmology and Nongalactic Astrophysics, Detection and Handling of Multicollinearity in Regression Analysis

File Description: application/pdf

Access URL: http://arxiv.org/abs/2306.06318
http://hdl.handle.net/10261/357713
https://doaj.org/article/a243d178fc194ca4b8b881726ab4d640
https://discovery-pp.ucl.ac.uk/id/eprint/10189030/

A probabilistic approach for failure localization

Authors: Panayiotou, Tania Chatzis, Sotirios P. Ellinas, Georgios et al.

Contributors: Panayiotou, Tania Chatzis, Sotirios P. Ellinas, Georgios et al.

Source: 2017 International Conference on Optical Network Design and Modeling (ONDM)

Subject Terms: Orthogonal frequency division multiplexing, Transparent optical networks, Fiber optic networks, Learning systems, OFDM networks, Engineering and Technology, Electrical Engineering - Electronic Engineering - Information Engineering, Fault Localization, Gaussian processes for Machine Learning

File Description: pdf

Access URL: https://zenodo.org/record/1036433/files/FL_ondm_v2.pdf
https://dblp.uni-trier.de/db/conf/ondm/ondm2017.html#PanayiotouCE17
https://ieeexplore.ieee.org/document/7958555
https://hdl.handle.net/20.500.14279/12644
https://api.elsevier.com/content/abstract/scopus_id/85025651276

The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data

Authors: Natália V. N. Rodrigues L. Raul Abramo Nina S. T. Hirata

Source: Machine Learning: Science and Technology, Vol 4, Iss 4, p 045019 (2023)

Subject Terms: FOS: Computer and information sciences, Computer engineering. Computer hardware, Computer Science - Machine Learning, Artificial intelligence, Outlier Detection, Machine Learning (stat.ML), Convolutional neural network, 02 engineering and technology, Noise (video), Clustering of Time Series Data and Algorithms, Weighting, Pattern recognition (psychology), 01 natural sciences, Machine Learning (cs.LG), TK7885-7895, Anomaly Detection in High-Dimensional Data, Gaussian Processes in Machine Learning, Machine Learning, Deep Learning, Statistics - Machine Learning, Artificial Intelligence, 0103 physical sciences, Machine learning, scientific data analysis, 0202 electrical engineering, electronic engineering, information engineering, Image (mathematics), Pattern Discovery, Data mining, Dimensionality Reduction, 4. Education, QA75.5-76.95, Data point, 15. Life on land, Computer science, statistical techniques, Algorithm, Electronic computers. Computer science, Signal Processing, Computer Science, Physical Sciences, machine learning methods, Medicine, Radiology

Access URL: http://arxiv.org/abs/2108.04742
https://doaj.org/article/1c9e036057d74ab7a1db9b3f611006aa

Inferring microscale properties of interacting systems from macroscale observations

Authors: Nazareno Campioni Dirk Husmeier Juan M. Morales et al.

Source: Physical Review Research, Vol 3, Iss 4, p 043074 (2021)

Subject Terms: 0301 basic medicine, Artificial intelligence, Scale (ratio), Complex system, QC1-999, Mathematical analysis, Quantum mechanics, 01 natural sciences, Gaussian Processes in Machine Learning, 03 medical and health sciences, Differential equation, Inference, Artificial Intelligence, Sparse Regression, Biochemistry, Genetics and Molecular Biology, FOS: Mathematics, 0101 mathematics, Gaussian process, Stochastic Thermodynamics and Fluctuation Theorems, Molecular Biology, Physics, Microscale chemistry, Life Sciences, Statistical and Nonlinear Physics, Computer science, Mathematics education, Process (computing), Stochasticity in Gene Regulatory Networks, Operating system, Physics and Astronomy, Computer Science, Physical Sciences, Gaussian, Fokker–Planck equation, Statistical physics, Mathematics

File Description: application/pdf

Access URL: http://link.aps.org/pdf/10.1103/PhysRevResearch.3.043074
https://eprints.gla.ac.uk/245037/1/245037.pdf
https://doaj.org/article/78d5eee653c94e72bef3e92056a63b0b
http://eprints.gla.ac.uk/245037/
https://link.aps.org/doi/10.1103/PhysRevResearch.3.043074

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs

Authors: Vincent Plassier Maxime Vono Alain Durmus et al.

Subject Terms: FOS: Computer and information sciences, Statistics and Probability, Computer Science - Machine Learning, Artificial intelligence, Computer Science - Artificial Intelligence, Cognitive Neuroscience, Bayesian Monte Carlo, Bayesian inference, Analysis of Brain Functional Connectivity Networks, Bayesian probability, Statistics - Computation, 01 natural sciences, Machine Learning (cs.LG), Methodology (stat.ME), Gaussian Processes in Machine Learning, Database, Theoretical computer science, Gibbs sampling, Inference, Artificial Intelligence, Computer security, FOS: Mathematics, 0101 mathematics, Key (lock), Statistics - Methodology, Computation (stat.CO), Adaptive MCMC, Statistics, Scalability, Life Sciences, Computer science, Monte Carlo method, Algorithm, Markov chain Monte Carlo, Artificial Intelligence (cs.AI), Physical Sciences, Computer Science, Bayesian Monte Carlo Methods in Scientific Inference, Mathematics, Neuroscience

Access URL: http://arxiv.org/abs/2106.06300