Bayesian approach to parameter estimation and interpolation of time-varying autoregressive processes using the Gibbs sampler

1997 ◽  
Vol 144 (4) ◽  
pp. 249 ◽  
Author(s):  
J.J. Rajan ◽  
P.J.W. Rayner ◽  
S.J. Godsill
Keyword(s):  
Parameter Estimation ◽  
Bayesian Approach ◽  
Gibbs Sampler ◽  
Time Varying ◽  
Autoregressive Processes

scholarly journals Sequential Parameter Estimation of Time-Varying Non-Gaussian Autoregressive Processes

2002 ◽  
Vol 2002 (8) ◽  
Author(s):  
Petar M. Djurić ◽  
Jayesh H. Kotecha ◽  
Fabien Esteve ◽  
Etienne Perret
Keyword(s):  
Parameter Estimation ◽  
Time Varying ◽  
Autoregressive Processes ◽  
Non Gaussian ◽  
Estimation Of Time

Parameter estimation of time-varying autoregressive models using the Gibbs sampler

1995 ◽  
Vol 31 (13) ◽  
pp. 1035-1036 ◽  
Author(s):  
J.J. Rajan ◽  
P.J.W. Rayner
Keyword(s):  
Parameter Estimation ◽  
Gibbs Sampler ◽  
Autoregressive Models ◽  
Time Varying ◽  
Estimation Of Time

A Bayesian approach for parameter estimation in multi-stage models

2018 ◽  
Vol 48 (10) ◽  
pp. 2459-2482 ◽  
Author(s):  
Hoa Pham ◽  
Darfiana Nur ◽  
Huong T. T. Pham ◽  
Alan Branford
Keyword(s):  
Parameter Estimation ◽  
Bayesian Approach ◽  
Stage Models ◽  
Multi Stage

Development of a time-varying downscaling model considering non-stationarity using a Bayesian approach

2018 ◽  
Vol 38 (7) ◽  
pp. 3157-3176 ◽  
Author(s):  
Subbarao Pichuka ◽  
Rajib Maity
Keyword(s):  
Bayesian Approach ◽  
Time Varying

scholarly journals Wavelet multi-resolution approximation of time-varying frame structure

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879559 ◽  
Author(s):  
Min Xiang ◽  
Feng Xiong ◽  
Yuanfeng Shi ◽  
Kaoshan Dai ◽  
Zhibin Ding
Keyword(s):  
Parameter Estimation ◽  
Degrees Of Freedom ◽  
Structural Parameters ◽  
Identification Problem ◽  
Frame Structure ◽  
Structural Parameter ◽  
Time Varying ◽  
Engineering Structures ◽  
Time Frequency ◽  
Non Parametric

Engineering structures usually exhibit time-varying behavior when subjected to strong excitation or due to material deterioration. This behavior is one of the key properties affecting the structural performance. Hence, reasonable description and timely tracking of time-varying characteristics of engineering structures are necessary for their safety assessment and life-cycle management. Due to its powerful ability of approximating functions in the time–frequency domain, wavelet multi-resolution approximation has been widely applied in the field of parameter estimation. Considering that the damage levels of beams and columns are usually different, identification of time-varying structural parameters of frame structure under seismic excitation using wavelet multi-resolution approximation is studied in this article. A time-varying dynamical model including both the translational and rotational degrees of freedom is established so as to estimate the stiffness coefficients of beams and columns separately. By decomposing each time-varying structural parameter using one wavelet multi-resolution approximation, the time-varying parametric identification problem is transformed into a time-invariant non-parametric one. In solving the high number of regressors in the non-parametric regression program, the modified orthogonal forward regression algorithm is proposed for significant term selection and parameter estimation. This work is demonstrated through numerical examples which consider both gradual variation and abrupt changes in the structural parameters.

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