DeepBayes—An estimator for parameter estimation in stochastic nonlinear dynamical models
Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications.
Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains
a challenging problem. The majority of existing methods employ maximum likelihood or
Bayesian estimation. However, these methods suffer from some limitations, most notably the
substantial computational time for inference coupled with limited flexibility in application. In
this work, we propose DeepBayes estimators that leverage the power of deep recurrent …
Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains
a challenging problem. The majority of existing methods employ maximum likelihood or
Bayesian estimation. However, these methods suffer from some limitations, most notably the
substantial computational time for inference coupled with limited flexibility in application. In
this work, we propose DeepBayes estimators that leverage the power of deep recurrent …
DeepBayes--an estimator for parameter estimation in stochastic nonlinear dynamical models
G Anubhab, M Abdalmoaty, S Chatterjee… - 2022 - diva-portal.org
Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications.
Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains
a challenging problem. The majority of existing methods employ maximum likelihood or
Bayesian estimation. However, these methods suffer from some limitations, most notably the
substantial computational time for inference coupled with limited flexibility in application. In
this work, we propose DeepBayes estimators that leverage the power of deep recurrent …
Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains
a challenging problem. The majority of existing methods employ maximum likelihood or
Bayesian estimation. However, these methods suffer from some limitations, most notably the
substantial computational time for inference coupled with limited flexibility in application. In
this work, we propose DeepBayes estimators that leverage the power of deep recurrent …