scholarly journals Efficient Bayesian estimation for GARCH-type models via sequential Monte Carlo

2020 ◽  
Author(s):  
Dan Li
Keyword(s):  
Monte Carlo ◽  
Bayesian Estimation ◽  
Sequential Monte Carlo

Bayesian Estimation of Long-Run Risk Models Using Sequential Monte Carlo

2020 ◽  
Author(s):  
Andras Fulop ◽  
Jeremy Heng ◽  
Junye Li ◽  
Hening Liu
Keyword(s):  
Monte Carlo ◽  
Bayesian Estimation ◽  
Sequential Monte Carlo ◽  
Risk Models ◽  
Long Run ◽  
Long Run Risk

Bayesian Estimation via Sequential Monte Carlo Sampling:  Unconstrained Nonlinear Dynamic Systems

2004 ◽  
Vol 43 (14) ◽  
pp. 4012-4025 ◽  
Author(s):  
Wen-shiang Chen ◽  
Bhavik R. Bakshi ◽  
Prem K. Goel ◽  
Sridhar Ungarala
Keyword(s):  
Monte Carlo ◽  
Bayesian Estimation ◽  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Sequential Monte Carlo ◽  
Monte Carlo Sampling ◽  
Nonlinear Dynamic Systems

State Space Modeling & Bayesian Inference with Computational Intelligence

2015 ◽  
Vol 11 (01) ◽  
pp. 71-101 ◽  
Author(s):  
Ayub Hanif ◽  
Robert Elliott Smith
Keyword(s):  
Monte Carlo ◽  
Bayesian Estimation ◽  
Computational Intelligence ◽  
Monte Carlo Methods ◽  
Sequential Monte Carlo ◽  
Sequential Monte Carlo Methods ◽  
Analysis And Design ◽  
Space Modeling ◽  
Recursive Bayesian Estimation ◽  
Recursive Bayesian

Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of nonlinear non-Gaussian dynamical systems. It enables us to reason under uncertainty and addresses shortcomings underlying deterministic systems and control theories which do not provide sufficient means of performing analysis and design. In addition, parametric techniques such as the Kalman filter and its extensions, though they are computationally efficient, do not reliably compute states and cannot be used to learn stochastic problems. We review recursive Bayesian estimation using sequential Monte Carlo methods highlighting open problems. Primary of these is the weight degeneracy and sample impoverishment problem. We proceed to detail synergistic computational intelligence sequential Monte Carlo methods which address this. We find that imbuing sequential Monte Carlos with computational intelligence has many advantages when applied to many application and problem domains.

scholarly journals Bayesian estimation via sequential Monte Carlo sampling—Constrained dynamic systems

Automatica ◽  
2007 ◽  
Vol 43 (9) ◽  
pp. 1615-1622 ◽  
Author(s):  
Lixin Lang ◽  
Wen-shiang Chen ◽  
Bhavik R. Bakshi ◽  
Prem K. Goel ◽  
Sridhar Ungarala
Keyword(s):  
Monte Carlo ◽  
Bayesian Estimation ◽  
Dynamic Systems ◽  
Sequential Monte Carlo ◽  
Monte Carlo Sampling
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