Bayesian inference in nonparametric dynamic state-space models

DOI10.1016/j.stamet.2014.02.004zbMath1486.62240arXiv1108.3262OpenAlexW2071485873MaRDI QIDQ1731225

Anurag Ghosh, Sandipan Roy, Soumalya Mukhopadhyay, Sourabh Bhattacharya

Publication date: 13 March 2019

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

Full work available at URL: https://arxiv.org/abs/1108.3262

zbMATH Keywords

Gaussian processMarkov chain Monte Carlostate-space modellook-up tableevolutionary equationobservational equation

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15)

Related Items (7)

Learning biological dynamics from spatio-temporal data by Gaussian processes ⋮ Unsupervised learning of observation functions in state space models by nonparametric moment methods ⋮ Bayesian inference in nonparametric dynamic state-space models ⋮ Robust identification of highly persistent interest rate regimes ⋮ Bayesian nonparametric analysis of multivariate time series: a matrix gamma process approach ⋮ Nonparametric dynamic state space modeling of observed circular time series with circular latent states: a Bayesian perspective ⋮ Bayesian nonparametric dynamic state space modeling with circular latent states

Uses Software

• astsa

Cites Work

• Unnamed Item
• Unnamed Item
• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• Bayesian forecasting and dynamic models.
• Time series: theory and methods
• Interpolation of spatial data. Some theory for kriging
• The design and analysis of computer experiments.
• Bayesian inference in nonparametric dynamic state-space models
• Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures
• Dynamic matrix-variate graphical models
• A simulation approach to Bayesian emulation of complex dynamic computer models

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