On markov chain monte carlo methods for nonlinear and non-gaussian state-space models

DOI10.1080/03610919908813583zbMath0968.62541OpenAlexW2128088998MaRDI QIDQ4488750

Hisashi Tanizaki, John F. Geweke

Publication date: 9 July 2000

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610919908813583

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (12)

Note on the Sampling Distribution for the Metropolis-Hastings Algorithm ⋮ Nonlinear and non-gaussian state estimation: A quasi-optimal estimator ⋮ Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data ⋮ Data assimilation for large‐scale spatio‐temporal systems using a location particle smoother ⋮ Modified Cholesky Riemann manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets ⋮ A Kushner-Stratonovich Monte Carlo filter applied to nonlinear dynamical system identification ⋮ Estimation of unknown parameters in nonlinear and non-Gaussian state-space models ⋮ Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling. ⋮ Achieving shrinkage in a time-varying parameter model framework ⋮ A Kalman particle filter for online parameter estimation with applications to affine models ⋮ Iterative construction of Gaussian process surrogate models for Bayesian inference ⋮ PDE-constrained optimization in medical image analysis

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