Bayesian identification of non-linear state-space models: Part I- Input design

IFAC Proceedings Volumes ◽

10.3182/20131218-3-in-2045.00105 ◽

2013 ◽

Vol 46 (32) ◽

pp. 774-779 ◽

Author(s):

Aditya Tulsyan ◽

Swanand R Khare ◽

Biao Huang ◽

R. Bhushan Gopaluni ◽

J. Fraser Forbes

Keyword(s):

State Space ◽

State Space Models ◽

Bayesian Identification ◽

Input Design ◽

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Bayesian identification of non-linear state-space models: Part II- Error analysis

IFAC Proceedings Volumes ◽

10.3182/20131218-3-in-2045.00106 ◽

2013 ◽

Vol 46 (32) ◽

pp. 631-636 ◽

Author(s):

Aditya Tulsyan ◽

Biao Huang ◽

R. Bhushan Gopaluni ◽

J. Fraser Forbes

Keyword(s):

Error Analysis ◽

State Space ◽

State Space Models ◽

Bayesian Identification ◽

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Non-linear state space models with partially specified distributions on states

Journal of Forecasting ◽

10.1002/for.3980090206 ◽

1990 ◽

Vol 9 (2) ◽

pp. 137-149 ◽

Author(s):

J. Q. Smith

Keyword(s):

State Space ◽

State Space Models ◽

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Bias compensation-based parameter and state estimation for a class of time-delay non-linear state-space models

IET Control Theory and Applications ◽

10.1049/iet-cta.2020.0104 ◽

2020 ◽

Vol 14 (15) ◽

pp. 2176-2185 ◽

Author(s):

Ya Gu ◽

Quanmin Zhu ◽

Hassan Nouri

Keyword(s):

State Estimation ◽

State Space ◽

State Space Models ◽

Linear State ◽

Bias Compensation

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Recursive prediction error identification and scaling of non-linear state space models using a restricted black box parameterization

10.1016/j.automatica.2005.08.017 ◽

2006 ◽

Vol 42 (1) ◽

pp. 159-168 ◽

Author(s):

Torbjörn Wigren

Keyword(s):

State Space ◽

Prediction Error ◽

Black Box ◽

State Space Models ◽

Error Identification ◽

Linear State ◽

Recursive Prediction

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Sampling Latent States for High-Dimensional Non-Linear State Space Models with the Embedded HMM Method

Bayesian Analysis ◽

10.1214/17-ba1077 ◽

2018 ◽

Vol 13 (3) ◽

pp. 797-822 ◽

Author(s):

Alexander Y. Shestopaloff ◽

Radford M. Neal

Keyword(s):

State Space ◽

State Space Models ◽

High Dimensional ◽

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A marginalised particle filter with variational inference for non‐linear state‐space models with Gaussian mixture noise

IET Radar Sonar & Navigation ◽

10.1049/rsn2.12179 ◽

2021 ◽

Author(s):

Cheng Cheng ◽

Jean‐Yves Tourneret ◽

Xiaodong Lu

Keyword(s):

Particle Filter ◽

State Space ◽

Gaussian Mixture ◽

Variational Inference ◽

State Space Models ◽

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On Gaussian Optimal Smoothing of Non-Linear State Space Models

IEEE Transactions on Automatic Control ◽

10.1109/tac.2010.2050017 ◽

2010 ◽

Vol 55 (8) ◽

pp. 1938-1941 ◽

Author(s):

Simo Sarkka ◽

Jouni Hartikainen

Keyword(s):

State Space ◽

State Space Models ◽

Optimal Smoothing ◽

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Simulation-based inference in non-linear state-space models: application to testing the permanent income hypothesis

Simulation-based Inference in Econometrics ◽

10.1017/cbo9780511751981.013 ◽

2000 ◽

pp. 218-234 ◽

Author(s):

Roberto S. Mariano ◽

Hisashi Tanizaki

Keyword(s):

State Space ◽

Permanent Income ◽

State Space Models ◽

Permanent Income Hypothesis ◽

Simulation Based ◽

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Robust Estimation in Non-Linear State-Space Models With State-Dependent Noise

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2014.2305636 ◽

2014 ◽

Vol 62 (8) ◽

pp. 2165-2175 ◽

Author(s):

Gabriel Agamennoni ◽

Eduardo M. Nebot

Keyword(s):

State Space ◽

Robust Estimation ◽

State Space Models ◽

State Dependent ◽

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A Rao-Blackwellized particle filter to estimate the time-varying noise parameters in non-linear state-space models using alternative stabilized forgetting

2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) ◽

10.1109/isspit.2016.7886040 ◽

2016 ◽

Author(s):

Milan Papez

Keyword(s):

Particle Filter ◽

State Space ◽

State Space Models ◽

Time Varying ◽

Noise Parameters ◽

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