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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...
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...