Efficient Bayesian model class selection of vector autoregressive models for system identification

2021 ◽  
Author(s):  
Jia‐Hua Yang ◽  
Qing‐Zhao Kong ◽  
Hong‐Jun Liu ◽  
Hua‐Yi Peng
Keyword(s):  
System Identification ◽  
Bayesian Model ◽  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Model Class ◽  
Model Class Selection ◽  
Selection Of

Algorithms for Bayesian Model Class Selection of Higher-Dimensional Dynamic Systems

2007 ◽  
Author(s):  
Sai Hung Cheung ◽  
James L. Beck
Keyword(s):  
Bayesian Model ◽  
Model Updating ◽  
Structural Dynamic ◽  
Higher Dimensional ◽  
Significant Probability ◽  
Model Class ◽  
Updating Procedure ◽  
Model Class Selection ◽  
Dimensional Integral ◽  
Selection Of

In recent years, Bayesian model updating techniques based on measured data have been applied in structural health monitoring. Often we are faced with the problem of how to select the ‘best’ model from a set of competing candidate model classes for the system based on data. To tackle this problem, Bayesian model class selection is used, which provides a rigorous Bayesian updating procedure to give the probability of different candidate classes for a system, based on the data from the system. There may be cases where more than one model class has significant probability and each of these will give different predictions. Bayesian model class averaging provides a coherent mechanism to incorporate all the considered model classes in the probabilistic predictions for the system. However, both Bayesian model class selection and Bayesian model class averaging require the calculation of the evidence of the model class which requires the nontrivial computation of a multi-dimensional integral. In this paper, several methods for solving this computationally challenging problem of model class selection are presented, proposed and compared. The efficiency of the proposed methods is illustrated by an example involving a structural dynamic system.

Exposing volatility spillovers: A comparative analysis based on vector autoregressive models

2016 ◽  
Vol 18 ◽  
pp. 302-305 ◽  
Author(s):  
Dionisis Philippas ◽  
Catalin Dragomirescu-Gaina
Keyword(s):  
Comparative Analysis ◽  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Volatility Spillovers

A Spatial Prior for Bayesian Vector Autoregressive Models

1999 ◽  
Vol 39 (2) ◽  
pp. 297-317 ◽  
Author(s):  
James P. LeSage ◽  
Anna Krivelyova
Keyword(s):  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive

Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus

2021 ◽  
pp. 1-24
Author(s):  
Yanling Li ◽  
Julie Wood ◽  
Linying Ji ◽  
Sy-Miin Chow ◽  
Zita Oravecz
Keyword(s):  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive

Bootstrap prediction bands for forecast paths from vector autoregressive models

2010 ◽  
Vol 30 (8) ◽  
pp. 721-735 ◽  
Author(s):  
Anna Staszewska-Bystrova
Keyword(s):  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Prediction Bands

scholarly journals Stationary Threshold Vector Autoregressive Models

2018 ◽  
Author(s):  
Galyna Grynkiv ◽  
Lars Stentoft
Keyword(s):  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive

scholarly journals Regime-Switching Global Vector Autoregressive Models

2013 ◽  
Author(s):  
Michael Binder ◽  
Marco Gross
Keyword(s):  
Regime Switching ◽  
Autoregressive Models ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Global Vector
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