Blind Deconvolution via Sequential Imputations
From MaRDI portal
Publication:4844190
DOI10.2307/2291068zbMath0826.62062OpenAlexW4241181106WikidataQ56503247 ScholiaQ56503247MaRDI QIDQ4844190
Publication date: 28 November 1995
Full work available at URL: https://doi.org/10.2307/2291068
communicationimportance samplingGibbs samplingsurvey samplingblind deconvolutionpredictive distributionmultiple imputationssimulation techniquessignal transmissionsequential imputationdiscrete input signalsadditive Gaussian noiseslinear moving average channelreal-time signal restorationrejuvenation procedure
Bayesian inference (62F15) Sequential statistical methods (62L99) Survival analysis and censored data (62N99) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Sequential estimation (62L12)
Related Items
Piecewise deterministic Markov processes for continuous-time Monte Carlo ⋮ Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models ⋮ Sequential Monte Carlo with Highly Informative Observations ⋮ Nonlinear and non-gaussian state estimation: A quasi-optimal estimator ⋮ Global Consensus Monte Carlo ⋮ The sample size required in importance sampling ⋮ A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design ⋮ A probabilistic graphical model approach to stochastic multiscale partial differential equations ⋮ A Survey of Sequential Monte Carlo Methods for Economics and Finance ⋮ Asymptotic genealogies of interacting particle systems with an application to sequential Monte Carlo ⋮ Efficient real-time monitoring of an emerging influenza pandemic: how feasible? ⋮ Multibody dynamic systems as Bayesian networks: Applications to robust state estimation of mechanisms ⋮ Efficient inference for nonlinear state space models: an automatic sample size selection rule ⋮ Rethinking the Effective Sample Size ⋮ On adaptive resampling strategies for sequential Monte Carlo methods ⋮ Truncation nonlinear filters for state estimation with nonlinear inequality constraints ⋮ ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification ⋮ A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data ⋮ Without-replacement sampling for particle methods on finite state spaces ⋮ Particle-based likelihood inference in partially observed diffusion processes using generalised Poisson estimators ⋮ Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics ⋮ Limit theorems for weighted samples with applications to sequential Monte Carlo methods ⋮ Particle filters and Bayesian inference in financial econometrics ⋮ Asymptotic properties of particle filter-based maximum likelihood estimators for state space models ⋮ Simultaneous eye tracking and blink detection with interactive particle filters ⋮ Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter ⋮ Generalized fiducial inference for normal linear mixed models ⋮ On parallel implementation of sequential Monte Carlo methods: the island particle model ⋮ On the role of interaction in sequential Monte Carlo algorithms ⋮ Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis ⋮ Forest resampling for distributed sequential Monte Carlo ⋮ Redrawing-resampling rejection controlled sequential importance sampling ⋮ Sequential state inference of engineering systems through the particle move-reweighting algorithm ⋮ Small-Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers ⋮ Simulation from quasi-stationary distributions on reducible state spaces ⋮ Speed of convergence for the blind deconvolution of a linear system with discrete random input ⋮ Ergodicity and accuracy of optimal particle filters for Bayesian data assimilation ⋮ Bayesian inference for Markov jump processes with informative observations ⋮ On resampling schemes for particle filters with weakly informative observations ⋮ Lookahead strategies for sequential Monte Carlo ⋮ Sampling, feasibility, and priors in data assimilation
