On particle methods for parameter estimation in state-space models

From MaRDI portal
Publication:254462

DOI10.1214/14-STS511zbMath1332.62096arXiv1412.8695OpenAlexW3103934441MaRDI QIDQ254462

Arnaud Doucet, Nikolas Kantas, Nicolas Chopin, Sumeetpal S. Singh, Jan M. Maciejowski

Publication date: 8 March 2016

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1412.8695




Related Items (87)

State estimation problem for the detection of valve closure in gas pipelinesConvergence of Regularized Particle Filters for Stochastic Reaction NetworksStatistical modelling of individual animal movement: an overview of key methods and a discussion of practical challengesAn algorithm for non-parametric estimation in state-space modelsSequential Monte Carlo smoothing with parameter estimationA Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space ModelsWhen artificial parameter evolution gets real: particle filtering for time-varying parameter estimation in deterministic dynamical systemsA tutorial on particle filtersOnline Smoothing for Diffusion Processes Observed with NoiseStochastic tail index model for high frequency financial data with Bayesian analysisInference for a class of partially observed point process modelsEstimation of agent-based models using sequential Monte Carlo methodsAnalysis of nonlinear state space model with dependent measurement noisesMaximum likelihood recursive state estimation using the expectation maximization algorithmIdentification of linear systems with multiplicative noise from multiple trajectory dataA particle-learning-based approach to estimate the influence matrix of online social networksSequential Bayesian inference for static parameters in dynamic state space modelsLikelihood-free stochastic approximation EM for inference in complex modelsA flexible state-space model for learning nonlinear dynamical systemsStochastic quasi-Newton with line-search regularisationOnline Bayesian inference and learning of Gaussian-process state-space modelsMultilevel Monte Carlo for Smoothing via Transport MethodsSequential Bayesian inference for vector autoregressions with stochastic volatilityAssessing ecosystem state space models: identifiability and estimationEfficient inference for nonlinear state space models: an automatic sample size selection ruleA stochastic variational framework for recurrent Gaussian processes modelsEfficient use of data for LSTM mortality forecastingBayesian parameter inference for partially observed stopped processesAdvanced Multilevel Monte Carlo MethodsA Wasserstein coupled particle filter for multilevel estimationOptimal filtering equations in state space model of the two factors mean reverting Ornstein-Uhlenbech processLatent Gaussian Count Time SeriesA Sample-Wise Data Driven Control Solver for the Stochastic Optimal Control Problem with Unknown Model ParametersEfficient data augmentation techniques for some classes of state space modelsSequential estimation of temporally evolving latent space network modelsA point mass proposal method for Bayesian state-space model fittingFisher Information Matrix for Single Molecules with Stochastic TrajectoriesVariance estimation for sequential Monte Carlo algorithms: a backward sampling approachThe divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theoremsA fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheetCoupling stochastic EM and approximate Bayesian computation for parameter inference in state-space modelsFast and Numerically Stable Particle-Based Online Additive Smoothing: The AdaSmooth AlgorithmData based quantification of synchronizationFourier series-based approximation of time-varying parameters in ordinary differential equationsDivide-and-conquer Bayesian inference in hidden Markov modelsComparison of simulation-based algorithms for parameter estimation and state reconstruction in nonlinear state-space modelsDoubly-online changepoint detection for monitoring health status during sports activitiesBellman filtering and smoothing for state-space modelsHeterogeneity of consumption responses to income shocks in the presence of nonlinear persistenceA novel system identification algorithm for nonlinear Markov jump systemOn coupling particle filter trajectoriesMaximum likelihood estimation of the Markov-switching GARCH model based on a general collapsing procedureTracking multiple moving objects in images using Markov chain Monte CarloUsing Monte Carlo Particle Methods to Estimate and Quantify Uncertainty in Periodic Parameters (Research)Biased online parameter inference for state-space modelsIdentification of stochastic nonlinear models using optimal estimating functionsNested particle filters for online parameter estimation in discrete-time state-space Markov modelsSmoothing With Couplings of Conditional Particle FiltersA family of multivariate non‐gaussian time series modelsA direct filter method for parameter estimationA method for high-dimensional smoothingModelling the joint behaviour of electricity prices in interconnected marketsBayesian Inference via Filtering Equations for Ultrahigh Frequency Data (I): Model and EstimationModeling and inference for infectious disease dynamics: a likelihood-based approachFrequentist delta-variance approximations with mixed-effects models and TMBCopula particle filtersParticle filters for partially-observed Boolean dynamical systemsMulti-vehicle tracking with microscopic traffic flow model-based particle filteringThree-dimensional random walk models of individual animal movement and their application to trap counts modellingA comparison of inferential methods for highly nonlinear state space models in ecology and epidemiologyOn Large Lag Smoothing for Hidden Markov ModelsBoolean Kalman filter and smoother under model uncertaintyGradient free parameter estimation for hidden Markov models with intractable likelihoodsA Kalman particle filter for online parameter estimation with applications to affine modelsOn the two-filter approximations of marginal smoothing distributions in general state-space modelsParticle-based online estimation of tangent filters with application to parameter estimation in nonlinear state-space modelsData assimilation: The Schrödinger perspectiveIdentification of MultiObject Dynamical Systems: Consistency and Fisher InformationUniform convergence over time of a nested particle filtering scheme for recursive parameter estimation in state-space Markov modelsUnnamed ItemSupervised learning from noisy observations: combining machine-learning techniques with data assimilationModel Error Estimation Using the Expectation Maximization Algorithm and a Particle Flow FilterBias of Particle Approximations to Optimal Filter DerivativeUniform Stability of a Particle Approximation of the Optimal Filter DerivativeFrequentist conditional variance for nonlinear mixed-effects modelsUnnamed ItemJoint Online Parameter Estimation and Optimal Sensor Placement for the Partially Observed Stochastic Advection-Diffusion Equation


Uses Software


Cites Work


This page was built for publication: On particle methods for parameter estimation in state-space models