Cover Image

An energy-based deep splitting method for the nonlinear filtering problem

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bågmark, Kasper, Andersson, Adam, Larsson, Stig
Format:	Preprint
Published:	2022
Subjects:	Computation Numerical Analysis Methodology Machine Learning 60G35, 62F15, 62G07, 62M20, 65C30, 65M75, 68T07
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Nonlinear filtering based on density approximation and deep BSDE prediction
by: Bågmark, Kasper, et al.
Published: (2025)

A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting
by: Bågmark, Kasper, et al.
Published: (2024)

High-dimensional Bayesian filtering through deep density approximation
by: Bågmark, Kasper, et al.
Published: (2025)

Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
by: Gyger, Tim, et al.
Published: (2024)

Solving the inverse source problem of the fractional Poisson equation by MC-fPINNs
by: Sheng, Rui, et al.
Published: (2024)

Convergence rates of non-stationary and deep Gaussian process regression
by: Osborne, Conor, et al.
Published: (2023)

Error analysis for empirical risk minimization over clipped ReLU networks in solving linear Kolmogorov partial differential equations
by: Xiao, Jichang, et al.
Published: (2023)

A Stabilized Physics Informed Neural Networks Method for Wave Equations
by: Jiao, Yuling, et al.
Published: (2024)

Mixture-Weighted Ensemble Kalman Filter with Quasi-Monte Carlo Transport
by: Klebanov, Ilja, et al.
Published: (2026)

Spatio-temporal probabilistic forecast using MMAF-guided learning
by: Bardi, Leonardo, et al.
Published: (2026)

Kernel Density Estimation and Convolution Revisited
by: Tenkorang, Nicholas, et al.
Published: (2025)

Model-free filtering in high dimensions via projection and score-based diffusions
by: Christensen, Sören, et al.
Published: (2025)

Density estimation for elliptic PDE with random input by preintegration and quasi-Monte Carlo methods
by: Gilbert, Alexander D., et al.
Published: (2024)

Rational approximation and intrinsic Gaussian processes
by: Beattie, Christopher, et al.
Published: (2026)

ICS for complex data with application to outlier detection for density data
by: Mondon, Camille, et al.
Published: (2025)

Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees
by: Limmer, Yannick, et al.
Published: (2024)

Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signals
by: Horvath, Blanka, et al.
Published: (2023)

Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation
by: Zabolotnii, Serhii
Published: (2026)

An effective estimation of multivariate density functions using extended-beta kernels with Bayesian adaptive bandwidths
by: Somé, Sobom M., et al.
Published: (2025)

Error analysis for learning fractional stochastic differential equations with applications in neural approximations
by: Dehshiri, Mahdi, et al.
Published: (2026)

Subordinated Wright-Fisher Priors
by: Judd, Nathan A., et al.
Published: (2026)

Operator Learning Using Random Features: A Tool for Scientific Computing
by: Nelsen, Nicholas H., et al.
Published: (2024)

Density estimation for compositional data using nonparametric mixtures
by: Xie, Jiajin, et al.
Published: (2025)

Comparing EPGP Surrogates and Finite Elements Under Degree-of-Freedom Parity
by: Amo, Obed, et al.
Published: (2025)

Scalable non-separable spatio-temporal Gaussian process models for large-scale short-term weather prediction
by: Gyger, Tim, et al.
Published: (2026)

Dynamical Low-Rank Ensemble Kalman filter for State/Parameter estimation
by: Nobile, Fabio, et al.
Published: (2026)

Kernel Density Machines
by: Della Vecchia, Andrea, et al.
Published: (2025)

Rejoinder to the discussion of "Mode-based estimation of the center of symmetry"
by: Chacón, José E., et al.
Published: (2025)

Amortized Energy-Based Bayesian Inference
by: Kaveh, Hojjat, et al.
Published: (2026)

Restricted Path Characteristic Function Determines the Law of Stochastic Processes
by: Li, Siran, et al.
Published: (2024)

Error Bounds for Importance Sampling with Estimated Proposal Distributions
by: Aeckerle-Willems, Cathrine, et al.
Published: (2026)

Fractal and Regular Geometry of Deep Neural Networks
by: Di Lillo, Simmaco, et al.
Published: (2025)

Autoencoders in Function Space
by: Bunker, Justin, et al.
Published: (2024)

A monotonic MM-type algorithm for estimation of nonparametric finite mixture models with dependent marginals
by: Levine, Michael
Published: (2025)

In almost all shallow analytic neural network optimization landscapes, efficient minimizers have strongly convex neighborhoods
by: Benning, Felix, et al.
Published: (2025)

A data-driven Fourier-mixture neural-network method for density estimation
by: Dang, Duy-Minh, et al.
Published: (2026)

Universal Adaptive Environment Discovery
by: Matymov, Madi, et al.
Published: (2025)

Optimizing Data Augmentation through Bayesian Model Selection
by: Matymov, Madi, et al.
Published: (2025)

A Novel Hybrid Approach for Time Series Forecasting: Period Estimation and Climate Data Analysis Using Unsupervised Learning and Spline Interpolation
by: Kayal, Tanmay, et al.
Published: (2025)

SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction
by: Lundström-Imanov, Gustav Olaf Yunus Laitinen-Fredriksson, et al.
Published: (2026)