Search Results - 62M45 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

File Description: application/xml

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

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

File Description: application/xml; application/pdf

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

EFFICIENT NEURAL NETWORK APPROACHES FOR CONDITIONAL OPTIMAL TRANSPORT WITH APPLICATIONS IN BAYESIAN INFERENCE.

Authors: ZHEYU OLIVER WANG BAPTISTA, RICARDO MARZOUK, YOUSSEF et al.

Source: SIAM Journal on Scientific Computing; 2025, Vol. 47 Issue 4, pC979-C1005, 27p

Subject Terms: ARTIFICIAL neural networks, BAYESIAN field theory, DYNAMIC programming, STATISTICAL sampling, MATHEMATICAL optimization, NUMERICAL analysis, MAXIMUM likelihood statistics

Ancestral graph Markov models: Ancestral graph Markov models.

Authors: Richardson, Thomas Spirtes, Peter

Source: Ann. Statist. 30, no. 4 (2002), 962-1030

Subject Terms: data-generating process, Combinatorial probability, DAG, 68R10, 68T30, Directed graphs (digraphs), tournaments, marginalizing and conditioning, 01 natural sciences, Directed acyclic graph, $m$-separation, Neural nets and related approaches to inference from stochastic processes, path diagram, Knowledge representation, Graph theory (including graph drawing) in computer science, 62M45, 60K99, MC-graph, summary graph, latent variable, 0101 mathematics, ancestral graph

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Access URL: https://projecteuclid.org/journals/annals-of-statistics/volume-30/issue-4/Ancestral-graph-Markov-models/10.1214/aos/1031689015.pdf
https://dialnet.unirioja.es/servlet/articulo?codigo=680258
https://core.ac.uk/display/21224753
https://projecteuclid.org/journals/annals-of-statistics/volume-30/issue-4/Ancestral-graph-Markov-models/10.1214/aos/1031689015.full
https://projecteuclid.org/download/pdf_1/euclid.aos/1031689015
http://projecteuclid.org/euclid.aos/1031689015
http://projecteuclid.org/euclid.aos/1031689015

Separation and Completeness Properties for Amp Chain Graph Markov Models: Separation and completeness properties for AMP chain graph Markov models.

Authors: Levitz, Michael Perlman, Michael D. Madigan, David B.

Source: Ann. Statist. 29, no. 6 (2001), 1751-1784

Subject Terms: Applications of graph theory, 68R10, 68T30, 02 engineering and technology, AMP model, 01 natural sciences, efficient algorithm, 62M45, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Statistics, Computational problems in statistics, $d$-separation, Neural nets and related approaches to inference from stochastic processes, Bayesian network, Bayesian networks, $p$-separation, completeness, Graph theory (including graph drawing) in computer science, chain graph, 60K99, Graphical Markov model, acyclic directed graph, Mathematics

File Description: application/xml; application/pdf

Access URL: https://projecteuclid.org/download/pdf_1/euclid.aos/1015345961
https://projecteuclid.org/euclid.aos/1015345961
https://www.stat.washington.edu/research/reports/2000/tr374.pdf
http://projecteuclid.org/euclid.aos/1015345961
https://academiccommons.columbia.edu/doi/10.7916/D8X34VJG
https://academiccommons.columbia.edu/doi/10.7916/D8348WM6/download
http://projecteuclid.org/euclid.aos/1015345961

FOURIER NEURAL NETWORK APPROXIMATION OF TRANSITION DENSITIES IN FINANCE.

Authors: RONG DU DUY-MINH DANG

Source: SIAM Journal on Scientific Computing; 2025, Vol. 47 Issue 2, pC529-C557, 29p

Subject Terms: CHARACTERISTIC functions, ARTIFICIAL neural networks, FINANCIAL economics, MARKET volatility, MEAN square algorithms, GAUSSIAN function, QUANTITATIVE research, DISTRIBUTION (Probability theory)

Deep Gaussian Processes for Calibration of Computer Models (with Discussion).

Authors: Marmin, Sébastien Filippone, Maurizio

Source: Bayesian Analysis; Dec2022, Vol. 17 Issue 4, p1301-1350, 30p

Subject Terms: GAUSSIAN processes, MARKOV processes, CALIBRATION, BAYESIAN analysis, GRAPHICS processing units

Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning.

Authors: Bolte, Jérôme Pauwels, Edouard

Source: Mathematical Programming. Jul2021, Vol. 188 Issue 1, p19-51. 33p.

Subject Terms: *AUTOMATIC differentiation, *SEMIALGEBRAIC sets, *DEEP learning, *ARTIFICIAL intelligence, *NONSMOOTH optimization, *NUMERICAL analysis, *STOCHASTIC convergence

Operator Learning Using Random Features: A Tool for Scientific Computing.

Authors: Nelsen, Nicholas H. Stuart, Andrew M.

Source: SIAM Review; 2024, Vol. 66 Issue 3, p535-571, 37p

Subject Terms: SCIENTIFIC computing, RANDOM operators, PARTIAL differential equations, KRIGING, SUPERVISED learning

PHYSICS GUIDED MACHINE LEARNING FOR VARIATIONAL MULTISCALE REDUCED ORDER MODELING.

Authors: AHMED, SHADY E. SAN, OMER RASHEED, ADIL et al.

Source: SIAM Journal on Scientific Computing; 2023, Vol. 45 Issue 3, pB283-B313, 31p

Subject Terms: MACHINE learning, PROPER orthogonal decomposition, PHYSICS

THE RANDOM FEATURE MODEL FOR INPUT-OUTPUT MAPS BETWEEN BANACH SPACES.

Authors: NELSEN, NICHOLAS H. STUART, ANDREW M.

Source: SIAM Journal on Scientific Computing; 2021, Vol. 43 Issue 5, pA3212-A3243, 32p

Subject Terms: BANACH spaces, BURGERS' equation, PARTIAL differential operators, PHYSICAL sciences, PARTIAL differential equations, MATHEMATICAL ability, ELLIPTIC equations

Applied Time Series Analysis and Forecasting with Python

Authors: Changquan Huang Alla Petukhina

Resource Type: eBook.

Subjects: Python (Computer program language), Time-series analysis, Time-series analysis--Forecasting, Time-series analysis--Computer programs

Categories: MATHEMATICS / Probability & Statistics / General, BUSINESS & ECONOMICS / Econometrics, BUSINESS & ECONOMICS / Statistics, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Languages / Python, COMPUTERS / Mathematical & Statistical Software

International Conference of Computational Methods in Sciences and Engineering (ICCMSE 2004)

Authors: Theodore Simos George Maroulis

Resource Type: eBook.

Subjects: Engineering mathematics--Congresses, Science--Mathematics--Congresses, Science--Data processing--Congresses, Engineering--Data processing--Congresses

Categories: COMPUTERS / Programming / Games, MATHEMATICS / General, TECHNOLOGY & ENGINEERING / General