Search Results - Neural nets and related approaches to inference from stochastic processes

Adaptive Deep Density Approximation for Stochastic Dynamical Systems: Adaptive deep density approximation for stochastic dynamical systems

Authors: Junjie He Qifeng Liao Xiaoliang Wan

Source: Journal of Scientific Computing. 102

Subject Terms: Numerical solutions to stochastic differential and integral equations, FOS: Computer and information sciences, continuity equation, stochastic dynamical systems, Machine Learning (stat.ML), Numerical Analysis (math.NA), Stochastic ordinary differential equations (aspects of stochastic analysis), Neural nets and related approaches to inference from stochastic processes, normalizing flows, 34F05, 60H35, 62M45, 65C30, deep neural networks, Statistics - Machine Learning, FOS: Mathematics, Mathematics - Numerical Analysis, Computational methods for stochastic equations (aspects of stochastic analysis), Artificial neural networks and deep learning

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Access URL: http://arxiv.org/abs/2405.02810
https://zbmath.org/7975330
https://doi.org/10.1007/s10915-025-02791-7

Stable convergence of heavy-/light-tailed infinitely wide neural networks: \(\alpha\)-stable convergence of heavy-/light-tailed infinitely wide neural networks

Authors: Paul Jung Hoil Lee Jiho Lee et al.

Source: Advances in Applied Probability. 55:1415-1441

Subject Terms: Neural nets and related approaches to inference from stochastic processes, multi-layer perceptrons, Stable stochastic processes, Central limit and other weak theorems, weak convergence, 0101 mathematics, heavy-tailed distribution, 01 natural sciences, Artificial neural networks and deep learning, stable process, infinite-width limit

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Access URL: https://zbmath.org/7806768
https://doi.org/10.1017/apr.2023.3

Towards end‐to‐end likelihood‐free inference with convolutional neural networks: Towards end-to-end likelihood-free inference with convolutional neural networks

Authors: Stefan T. Radev Ulf K. Mertens Andreas Voss et al.

Source: British Journal of Mathematical and Statistical Psychology. 73:23-43

Subject Terms: Likelihood Functions, Bayesian inference, 05 social sciences, leaky competing accumulator, Bayes Theorem, 01 natural sciences, Causal inference from observational studies, convolutional network, Neural nets and related approaches to inference from stochastic processes, Machine Learning, approximate Bayesian computation, machine learning, likelihood-free inference, Humans, Regression Analysis, Computer Simulation, 0501 psychology and cognitive sciences, Neural Networks, Computer, 0101 mathematics, Computational methods for problems pertaining to statistics, Algorithms, Applications of statistics to psychology

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Access URL: https://pubmed.ncbi.nlm.nih.gov/30793299
https://zbmath.org/7239909
https://doi.org/10.1111/bmsp.12159
https://www.onlinelibrary.wiley.com/doi/abs/10.1111/bmsp.12159
https://www.onlinelibrary.wiley.com/doi/pdf/10.1111/bmsp.12159
https://www.ncbi.nlm.nih.gov/pubmed/30793299
https://pubmed.ncbi.nlm.nih.gov/30793299/
https://bpspsychub.onlinelibrary.wiley.com/doi/abs/10.1111/bmsp.12159
https://europepmc.org/article/MED/30793299

Machine learning-based conditional mean filter: A generalization of the ensemble Kalman filter for nonlinear data assimilation: Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation

Authors: Hoang, Truong-Vinh Krumscheid, Sebastian Matthies, Hermann G. et al.

Contributors: Hoang, Truong-Vinh Krumscheid, Sebastian Matthies, Hermann G. et al.

Source: Foundations of Data Science. 5:56-80

Subject Terms: ddc:004, FOS: Computer and information sciences, Computer Science - Machine Learning, Learning and adaptive systems in artificial intelligence, Machine Learning (stat.ML), 62M45, 62M20, 65C20, 86-08, Statistics - Computation, 01 natural sciences, Inference from stochastic processes and prediction, Machine Learning (cs.LG), Statistics - Machine Learning, conditional expectation, FOS: Mathematics, Mathematics - Numerical Analysis, Computation (stat.CO), 0105 earth and related environmental sciences, DATA processing & computer science, weather forecast, deep learning, Monte Carlo methods, Numerical Analysis (math.NA), Filtering in stochastic control theory, Neural nets and related approaches to inference from stochastic processes, nonlinear filter, inverse problem

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Access URL: http://arxiv.org/abs/2106.07908

Minimal embedding dimensions of connected neural codes

Authors: Ngoc Mai Tran Raffaella Mulas

Source: Algebraic Statistics. 11:99-106

Subject Terms: receptive field code, minimal embedding, 02 engineering and technology, Neural networks for/in biological studies, artificial life and related topics, Hypergraphs, 01 natural sciences, Algebraic statistics, Applications of statistics to biology and medical sciences, meta analysis, Planar graphs, geometric and topological aspects of graph theory, Neural nets and related approaches to inference from stochastic processes, realizability, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Mathematics - Combinatorics, Neurons and Cognition (q-bio.NC), Combinatorics (math.CO), 0101 mathematics

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Access URL: https://msp.org/astat/2020/11-1/astat-v11-n1-p06-s.pdf
http://arxiv.org/abs/1706.03999
https://arxiv.org/pdf/1706.03999.pdf
https://arxiv.org/abs/1706.03999
http://ui.adsabs.harvard.edu/abs/2017arXiv170603999M/abstract
https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3315112
https://msp.org/astat/2020/11-1/p06.xhtml
https://msp.org/astat/2020/11-1/astat-v11-n1-p06-s.pdf

A review of regularised estimation methods and cross-validation in spatiotemporal statistics

Authors: Fassò, Alessandro Otto, Philipp Maranzano, Paolo

Source: Statistics Surveys. 18

Subject Terms: FOS: Computer and information sciences, Big data, dimension reduction, geostatistics, spatial econometrics, Ridge regression, shrinkage estimators (Lasso), Other Statistics (stat.OT), Research exposition (monographs, survey articles) pertaining to statistics, Statistics - Computation, Neural nets and related approaches to inference from stochastic processes, Methodology (stat.ME), Statistics - Other Statistics, big data, Applications of statistics to environmental and related topics, Design of statistical experiments, Directional data, spatial statistics, Statistics - Methodology, Computation (stat.CO)

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Access URL: http://arxiv.org/abs/2402.00183

Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks

Authors: Maciej Jaworski Piotr Duda Leszek Rutkowski

Source: Information Sciences. :497-518

Subject Terms: Neural nets and related approaches to inference from stochastic processes, convergence in probability and with probability one, regression function, Learning and adaptive systems in artificial intelligence, time-varying environment, 0202 electrical engineering, electronic engineering, information engineering, stream data, Nonparametric regression and quantile regression, 02 engineering and technology, orthogonal series, 16. Peace & justice

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Access URL: https://zbmath.org/7245702
https://doi.org/10.1016/j.ins.2017.07.013
https://www.sciencedirect.com/science/article/pii/S0020025517308344
https://dblp.uni-trier.de/db/journals/isci/isci460.html#DudaJR18

The accuracy of a procedural approach to specifying feedforward neural networks for forecasting: The accuracy of a procedural approach to specifying feedforward neural networks for fore\-casting

Authors: Liao, K P Fildes, R A

Source: Computers & Operations Research. 32:2151-2169

Subject Terms: Neural nets and related approaches to inference from stochastic processes, Time series, auto-correlation, regression, etc. in statistics (GARCH), 0502 economics and business, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, Comparative forecasting accuracy, Model specification, Backpropagation, 02 engineering and technology, Neural networks, Inference from stochastic processes and prediction

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Access URL: https://eprints.lancs.ac.uk/id/eprint/48697/1/Document.pdf

Sieve Estimates via Neural Network for Strong Mixing Processes: Sieve estimates via neural network for strong mixing processes

Authors: Jiantong Zhang

Source: Statistical Inference for Stochastic Processes. 7:115-135

Subject Terms: Neural nets and related approaches to inference from stochastic processes, convergence rate, neural network, Asymptotic properties of nonparametric inference, mixing processes, sieves estimate, 0101 mathematics, Non-Markovian processes: estimation, 01 natural sciences

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Access URL: https://zbmath.org/2128436
https://doi.org/10.1023/b:sisp.0000026042.34760.1e
https://link.springer.com/content/pdf/10.1023/B:SISP.0000026042.34760.1e.pdf
https://econpapers.repec.org/RePEc:spr:sistpr:v:7:y:2004:i:2:p:115-135
https://ideas.repec.org/a/spr/sistpr/v7y2004i2p115-135.html

Learning the Mapping $$\mathbf {x}\mapsto \sum\limits _{i=1}^d x_i^2$$: the Cost of Finding the Needle in a Haystack: Learning the mapping \(\mathbf{x}\mapsto \sum\limits_{i=1}^d x_i^2\): the cost of finding the needle in a haystack

Authors: Leonardo Zepeda-Núñez Yuan Yao Jiefu Zhang et al.

Source: Communications on Applied Mathematics and Computation. 3:313-335

Subject Terms: Neural nets and related approaches to inference from stochastic processes, 0301 basic medicine, sample complexity, 03 medical and health sciences, Statistical aspects of big data and data science, machine learning, Numerical summation of series, Learning and adaptive systems in artificial intelligence, 0202 electrical engineering, electronic engineering, information engineering, generalization error, 02 engineering and technology

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Access URL: https://zbmath.org/7432645
https://doi.org/10.1007/s42967-020-00078-2
https://www.camc.shu.edu.cn/EN/10.1007/s42967-020-00078-2
https://link.springer.com/article/10.1007/s42967-020-00078-2
https://link.springer.com/content/pdf/10.1007/s42967-020-00078-2.pdf

NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches

Authors: Gao, Y Er, MJ

Source: Fuzzy Sets and Systems. 150:331-350

Subject Terms: Neural nets and related approaches to inference from stochastic processes, Time series, auto-correlation, regression, etc. in statistics (GARCH), Fuzzy control/observation systems, NARMAX models, 0202 electrical engineering, electronic engineering, information engineering, Fuzzy neural networks, 02 engineering and technology, Time series prediction, Takagi-Sugeno-Kang fuzzy inference systems, Inference from stochastic processes and prediction

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An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based neural approaches for classification: An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based neural approaches for classification.

Authors: Parag C. Pendharkar James A. Rodger

Source: Computers & Operations Research. 31:481-498

Subject Terms: Neural nets and related approaches to inference from stochastic processes, Classification and discrimination, cluster analysis (statistical aspects), 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Genetic algorithms, Approximation methods and heuristics in mathematical programming, Discriminant analysis, Neural networks

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Access URL: https://zbmath.org/2055438
https://doi.org/10.1016/s0305-0548(02)00229-0
https://dblp.uni-trier.de/db/journals/cor/cor31.html#PendharkarR04
https://dl.acm.org/doi/10.1016/S0305-0548%2802%2900229-0
https://pennstate.pure.elsevier.com/en/publications/an-empirical-study-of-impact-of-crossover-operators-on-the-perfor
https://www.sciencedirect.com/science/article/pii/S0305054802002290

Bayesian Representation of Stochastic Processes under Learning: de Finetti Revisited: Bayesian representation of stochastic processes under learning: de Finetti revisited.

Authors: Matthew O. Jackson Ehud Kalai Rann Smorodinsky

Source: Econometrica. 67:875-893

Subject Terms: Neural nets and related approaches to inference from stochastic processes, Inference from stochastic processes, ddc:330, Bayesian inference, Exchangeability for stochastic processes, 0502 economics and business, 05 social sciences

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Access URL: http://www.kellogg.northwestern.edu/research/math/papers/1228.pdf
https://zbmath.org/1746768
https://doi.org/10.1111/1468-0262.00055
https://onlinelibrary.wiley.com/doi/abs/10.1111/1468-0262.00055
http://doi.wiley.com/10.1111/1468-0262.00055
https://EconPapers.repec.org/RePEc:ecm:emetrp:v:67:y:1999:i:4:p:875-894
https://www.scholars.northwestern.edu/en/publications/bayesian-representation-of-stochastic-processes-under-learning-de
https://ideas.repec.org/a/ecm/emetrp/v67y1999i4p875-894.html
https://onlinelibrary.wiley.com/doi/10.1111/1468-0262.00055/pdf
http://hdl.handle.net/10419/221584

A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem

Authors: Parag C. Pendharkar

Source: Computers & Operations Research. 32:2561-2582

Subject Terms: Applications of statistics to actuarial sciences and financial mathematics, Artificial intelligence, Classification and discrimination, cluster analysis (statistical aspects), Artificial neural networks, Learning and adaptive systems in artificial intelligence, 0211 other engineering and technologies, 02 engineering and technology, Genetic algorithms, Discriminant analysis, Neural nets and related approaches to inference from stochastic processes, 0202 electrical engineering, electronic engineering, information engineering, Learning, Bankruptcy prediction, Classification problem

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Access URL: https://pennstate.pure.elsevier.com/en/publications/a-threshold-varying-artificial-neural-network-approach-for-classi
http://www.sciencedirect.com/science/article/pii/S0305054804001558
http://dblp.uni-trier.de/db/journals/cor/cor32.html#Pendharkar05
https://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16683593
https://dl.acm.org/doi/10.1016/j.cor.2004.06.023
https://www.sciencedirect.com/science/article/pii/S0305054804001558

Bayesian inference in neural networks

Authors: Robert L. Paige Ronald W. Butler

Source: Biometrika. 88:623-641

Subject Terms: Neural nets and related approaches to inference from stochastic processes, Laplace approximations, model choice, 0504 sociology, neural network models, Bayesian inference, 05 social sciences, General nonlinear regression, prediction, Bayesian computation, 0101 mathematics, 01 natural sciences

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Access URL: https://zbmath.org/1713848
https://doi.org/10.1093/biomet/88.3.623
https://www.jstor.org/stable/2673435
https://academic.oup.com/biomet/article-abstract/88/3/623/340098/
https://scholarsmine.mst.edu/math_stat_facwork/340/

stability of stochastic neural networks: Stability of stochastic neural networks.

Authors: Xuerong Mao X. X. Liao

Source: Neural, Parallel & Scientific Computations archive. 4(2):205-224

Subject Terms: Neural nets and related approaches to inference from stochastic processes, Structural stability and analogous concepts of solutions to ordinary differential equations, Ordinary differential equations and systems with randomness, exponential stability, deterministic neural networks, Learning and adaptive systems in artificial intelligence, stochastic perturbation, Neural networks for/in biological studies, artificial life and related topics, stochastic neural networks, Stochastic ordinary differential equations (aspects of stochastic analysis)

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Access URL: https://zbmath.org/2169898
https://zbmath.org/1424952
https://dl.acm.org/doi/10.5555/229526.229538
https://dblp.uni-trier.de/db/journals/npsc/npsc4.html#LiaoM96

numerical analysis near singularities in rbf networks: Numerical analysis near singularities in RBF networks

Authors: Guo, Weili Ong, Yew-Soon Hervas, Jaime Rubio et al.

Contributors: Guo, Weili Ong, Yew-Soon Hervas, Jaime Rubio et al.

Source: Journal of Machine Learning Research. 19(1):1-39

Subject Terms: Engineering::Computer science and engineering [DRNTU], Neural nets and related approaches to inference from stochastic processes, learning dynamics, Singularity, numerical analysis, RBF networks, deep learning, Numerical aspects of recurrence relations, Statistical aspects of information-theoretic topics, singularity, RBF Networks, Artificial neural networks and deep learning

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Access URL: https://zbmath.org/6982292
https://dr.ntu.edu.sg/handle/10220/46400
https://jmlr.org/papers/v19/16-210.html
https://dblp.uni-trier.de/db/journals/jmlr/jmlr19.html#GuoWOHZWZ18
https://jmlr.org/papers/volume19/16-210/16-210.pdf
https://dl.acm.org/doi/10.5555/3291125.3291126
https://jmlr.csail.mit.edu/papers/v19/16-210.html
http://www.jmlr.org/papers/volume19/16-210/16-210.pdf
https://hdl.handle.net/10356/89770
http://hdl.handle.net/10220/46400

Investigation of Possible Neural Architectures Underlying Information-Geometric Measures: Investigation of possible neural architectures underlying information-geometric measures

Authors: Masato Okada Masami Tatsuno

Source: Neural Computation. 16:737-765

Subject Terms: Neurons, 0301 basic medicine, 0303 health sciences, Models, Neurological, Action Potentials, connected networks, Neural networks for/in biological studies, artificial life and related topics, Synaptic Transmission, firing rates, Neural nets and related approaches to inference from stochastic processes, 03 medical and health sciences, Neural Pathways, Animals, Neural Networks, Computer, Nerve Net

File Description: application/xml

Access URL: https://pubmed.ncbi.nlm.nih.gov/15025828
https://zbmath.org/2106272
https://doi.org/10.1162/089976604322860686

Neural network forecasting for seasonal and trend time series

Authors: Min Qi G.Peter Zhang

Source: European Journal of Operational Research. 160:501-514

Subject Terms: Neural nets and related approaches to inference from stochastic processes, Time series, auto-correlation, regression, etc. in statistics (GARCH), Time series, Box-Jenkins method, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Seasonality, 02 engineering and technology, Neural networks, Inference from stochastic processes and prediction, Forecasting

File Description: application/xml

Sensitivity of Spatial Analysis Neural Network Training and Interpolation to Structural Parameters: Sensitivity of spatial analysis neural network training and interpolation to structural parame\-ters

Authors: Jose D. Salas Timothy R. Green Ana Martinez

Source: Mathematical Geology. 36:721-742

Subject Terms: Neural nets and related approaches to inference from stochastic processes, 2. Zero hunger, Inference from spatial processes, 0401 agriculture, forestry, and fisheries, Geostatistics, spatial interpolation, 04 agricultural and veterinary sciences, Applications of statistics to environmental and related topics, Nonparametric estimation, 01 natural sciences, 0105 earth and related environmental sciences

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Access URL: https://zbmath.org/2174075
https://doi.org/10.1023/b:matg.0000039543.89653.57
https://link.springer.com/article/10.1023/B%3AMATG.0000039543.89653.57
https://link.springer.com/10.1023/B:MATG.0000039543.89653.57