S. Fruwirth-Schnatter. Bayesian model discrimination and Bayes factors for linear Gaussian state space models. J. Royal Stat. Soc. B, 57:237-246, 1995.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....model. where A and C are the state transition and emission matrices and w t and v t are state and output noise. It is straightforward to generalise this to a linear system driven by some observed inputs, u t . A Bayesian analysis of state space models using MCMC methods can be found in [4]. The complete data likelihood for state space models is Gaussian, which falls within the class of exponential family distributions. In order to derive a variational Bayesian algorithm by applying the results in the previous section we now turn to de ning conjugate priors over the parameters. ....

S. Fruwirth-Schnatter. Bayesian model discrimination and Bayes factors for linear Gaussian state space models. J. Royal Stat. Soc. B, 57:237-246, 1995.

....model. where A and C are the state transition and emission matrices and w t and v t are state and output noise. It is straightforward to generalise this to a linear system driven by some observed inputs, u t . A Bayesian analysis of state space models using MCMC methods can be found in [4]. The complete data likelihood for state space models is Gaussian, which falls within the class of exponential family distributions. In order to derive a variational Bayesian algorithm by applying the results in the previous section we now turn to de ning conjugate priors over the parameters. ....

S. Fruwirth-Schnatter. Bayesian model discrimination and Bayes factors for linear Gaussian state space models. J. Royal. Stat. Soc. B, 57:237-246, 1995.

....model. where A and C are the the state transition and emission matrices and w t and v t are state and output noise. It is straightforward to generalise this to a linear system driven by some observed inputs, u t . A Bayesian analysis of state space models using MCMC methods can be found in [3]. The complete data likelihood for state space models is Gaussian, which falls within the class of exponential family distributions. In order to derive a variational Bayesian algorithm by applying the results in the previous section we now turn to de ning conjugate priors over the parameters. ....

S. Fruwirth-Schnatter. Bayesian model discrimination and Bayes factors for linear Gaussian state space models. J. Royal. Stat. Soc. B, 57:237-246, 1995.

....an adequate solution to this problem for nonlinear dynamical systems requires further research. The rst idea is the use of Markov chain Monte Carlo techniques to sample over both parameters and hidden variables. MCMC methods such as Gibbs sampling have been used for linear dynamical systems [44] [45] while a promising method for nonlinear systems is particle ltering [20] 26] The second idea is the use of so called automatic relevance determination (ARD; 46] 47] This consists of using a zero mean Gaussian prior on each parameter with tunable variances. Optimizing these variance ....

S. Fruwirth-Schnatter, \Bayesian model discrimination and Bayes factors for linear Gaussian state space models," Journal of the Royal Statistical Society, Series B, vol. 57, pp. 237-246, 1995.

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Fruhwirth-Schnatter, S. (1995). "Bayesian model discrimination and Bayes factors for linear Gaussian state space models," Journal of the Royal Statistical Society, Series B, 57, 237-246.

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