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MCMC for non-linear state space models using ensembles of latent sequences
MCMC non-linear state space models ensembles latent sequences
2013/6/13
Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no str...
Propagation of initial errors on the parameters for linear and Gaussian state space models
Kalman filter Extended Kalman filter State space mod-els Autoregressive process
2013/4/27
For linear and Gaussian state space models parametrized by $\theta_0 \in \Theta \subset \R^{r}, r \geq 1$ corresponding to the vector of parameters of the model, the Kalman filter gives exactly the so...
Non-asymptotic deviation inequalities for smoothed additive functionals in non-linear state-space models with applications to parameter estimation
Non-asymptotic deviation inequalities smoothed additive functionals in non-linear state-space parameter estimation
2011/2/22
Approximating joint smoothing distributions using particle-based methods is a well-known issue in statistical inference when operating on general state space hidden Markov models (HMM). In this paper ...
Convex Optimization In Identification Of Stable Non-Linear State Space Models
Convex Optimization Identification Stable Non-Linear State Space Models
2010/12/1
A new framework for nonlinear system identification is presented in terms of optimal fitting of stable nonlinear state space equations to input/output/state data, with a performance objective defined ...
On some problems in the article “Efficient Likelihood Estimation in State Space Models” by Cheng-Der Fuh
problems Efficient Likelihood Estimation State Space Models
2010/3/11
Upon reading the paper Efficient Likelihood Estimation
in State Space Models by Cheng-Der Fuh I found a number of problems in the
formulations and a number of mathematical errors. Together, these fi...
Multivariate stochastic volatility using state space models
Multivariate stochastic volatility state space models
2010/12/13
A Bayesian procedure is developed for multivariate stochastic volatility, using state space models. An autoregressive model for the log-returns is employed. We generalize the inverted Wishart distrib...