scientific article

Nando de Freitas, Arnaud Doucet, Neil A. Gordon

Publication date: 23 October 2001

Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.

Monte Carlo methods (65C05) Application models in control theory (93C95)

Related Items

Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis, Exploring the conformational space for protein folding with sequential Monte Carlo, Sequential ensemble transform for Bayesian inverse problems, Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges, Kernel learning backward SDE filter for data assimilation, Efficient particle smoothing for Bayesian inference in dynamic survival models, Importance Sampling and Necessary Sample Size: An Information Theory Approach, A tutorial on particle filters, On stability and convergence of optimal estimation for networked control systems with dual packet losses without acknowledgment, Stability analysis of extended, cubature and unscented Kalman filters for estimating stiff continuous-discrete stochastic systems, A comparison of nonlinear extensions to the ensemble Kalman filter. Gaussian anamorphosis and two-step ensemble filters, PORTFOLIO INSURANCE UNDER ROUGH VOLATILITY AND VOLTERRA PROCESSES, Constrained ensemble Langevin Monte Carlo, Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions, An ergodic theorem for the weighted ensemble method, A multi-rate multiple model track-before-detect particle filter, Overall hyperbolic-singular-value-decomposition-based square-root solutions in Kalman filters with deterministically sampled mean and covariance for state estimation in continuous-discrete nonlinear stochastic systems, A stochastic variant of the EM algorithm to fit mixed (discrete and continuous) longitudinal data with nonignorable missingness, A Two-Step Branching Splitting Model Under Cost Constraint for Rare Event Analysis, Inference for differential equation models using relaxation via dynamical systems, Online Bayesian learning for mixtures of spatial spline regressions with mixed effects, Optimal point process filtering and estimation of the coalescent process, Adaptive Semiparametric Bayesian Differential Equations Via Sequential Monte Carlo, Qualitative Robustness in Bayesian Inference, Scaling up Bayesian variational inference using distributed computing clusters, A Bayesian nonparametric approach to dynamical noise reduction, Book Review: Fundamentals of stochastic filtering, Estimation in the partially observed stochastic Morris-Lecar neuronal model with particle filter and stochastic approximation methods, Daisee: Adaptive importance sampling by balancing exploration and exploitation, Gaussian kernel quadrature Kalman filter, Latent Gaussian Count Time Series, On spatially correlated observations in importance sampling methods for subsidence estimation, A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet, A subdiffusive stochastic volatility jump model, Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data, Structured filtering, Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy, Stein variational gradient descent: many-particle and long-time asymptotics, A novel stochastically stable variational Bayesian Kalman filter for spacecraft attitude estimation, Particle filter-based data assimilation technique for the evaluation of transport of pollutants in small rivers, A Defensive Marginal Particle Filtering Method for Data Assimilation, Particle Filters for nonlinear data assimilation in high-dimensional systems, Nested particle filters for online parameter estimation in discrete-time state-space Markov models, Subsampling sequential Monte Carlo for static Bayesian models, Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers, Particle-kernel estimation of the filter density in state-space models, Stochastic Filtering Methods in Electronic Trading, Dynamic quantile linear models: a Bayesian approach, A parallel search strategy based on sparse representation for infrared target tracking, Grid methods for Bayes-optimal continuous-discrete filtering and utilizing a functional tensor train representation, Adaptive Time Stepping Methods Within a Data Assimilation Framework Applied to Non-isothermal Flow Dynamics, Latent binary MRF for online reconstruction of large scale systems, Particle filters for partially-observed Boolean dynamical systems, On the effectiveness of Monte Carlo for initial uncertainty forecasting in nonlinear dynamical systems, Effective branching splitting method under cost constraint, Importance sampling: intrinsic dimension and computational cost, Addressing the curse of dimensionality in stochastic dynamics: a Wiener path integral variational formulation with free boundaries, The Wigner Branching Random Walk: Efficient Implementation and Performance Evaluation, Asymptotic properties of particle filter-based maximum likelihood estimators for state space models, Filtering and estimation for a class of stochastic volatility models with intractable likelihoods, Assimilating Eulerian and Lagrangian data in traffic-flow models, Derivative-free estimation methods: new results and performance analysis, Model-based quantification of left ventricular diastolic function in critically ill patients with atrial fibrillation from routine data: a feasibility study, Reuse, recycle, reweigh: combating influenza through efficient sequential Bayesian computation for massive data, Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime, Online state estimation for discrete nonlinear dynamic systems with nonlinear noise and interference, Maximum likelihood estimation of the Cox-Ingersoll-Ross model using particle filters, Ensemble Kalman inversion: mean-field limit and convergence analysis, Ensemble Kalman methods with constraints, A generalized multi-fidelity simulation method using sparse polynomial chaos expansion, A duality formula and a particle Gibbs sampler for continuous time Feynman-Kac measures on path spaces, Analysis of single particle diffusion with transient binding using particle filtering, On the convergence of two sequential Monte Carlo methods for maximum a posteriori sequence estimation and stochastic global optimization, Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective, Nudging the particle filter, Fast simulated annealing in \(\mathbb R^d\) with an application to maximum likelihood estimation in state-space models, Bayesian Conditional Density Filtering, Uniform convergence over time of a nested particle filtering scheme for recursive parameter estimation in state-space Markov models, Subsampling MCMC -- an introduction for the survey statistician, Ensemble Kalman inversion for nonlinear problems: weights, consistency, and variance bounds, Unnamed Item, Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework, Ergodicity and accuracy of optimal particle filters for Bayesian data assimilation, Parametric estimation of hidden Markov models by least squares type estimation and deconvolution, Ensemble Kalman Sampler: Mean-field Limit and Convergence Analysis, Accelerated Bayesian inference-based history matching of petroleum reservoirs using polynomial chaos expansions, Unnamed Item, Bayesian state estimation in the presence of slow-rate integrated measurement, Approximate nonlinear filtering and its application in navigation, Altering Gaussian process to Student-t process for maximum distribution construction