by the literature using two main strategies: data transformation and diffuse likelihood. The data transformation approach … is cumbersome, as it requires nonstandard filtering. On the other hand, in some nontrivial cases the diffuse likelihood …

diffuse likelihood is a marginal likelihood for a specific data transformation that may depend on parameters. Therefore, the … diffuse likelihood can not be used generally for parameter estimation. Our newly proposed marginal likelihood function is … evaluated using the Kalman filter. The diffuse Kalman filter is specifically designed for computing the diffuse likelihood …

diffuse likelihood is a marginal likelihood for a specific data transformation that may depend on parameters. Therefore, the … diffuse likelihood can not be used generally for parameter estimation. Our newly proposed marginal likelihood function is … evaluated using the Kalman filter. The diffuse Kalman filter is specifically designed for computing the diffuse likelihood …

likelihood is a marginal likelihood for a specific data transformation that may depend on parameters. Therefore, the diffuse … Kalman filter. The diffuse Kalman filter is specifically designed for computing the diffuse likelihood function. We show that … that the diffuse likelihood function is not appropriate for a range of state space model specifications. …

diffuse likelihood is a marginal likelihood for a specific data transformation that may depend on parameters. Therefore, the … diffuse likelihood can not be used generally for parameter estimation. Our newly proposed marginal likelihood function is … evaluated using the Kalman filter. The diffuse Kalman filter is specifically designed for computing the diffuse likelihood …