… to estimate time-varying parameters by posterior modes. A generalization of the extended
Kalman filter and … is suggested for approximate posterior mode estimation. For the purpose of …

… In Section 4, the estimation problem is taken up from a different point of view. We consider
posterior mode estimation of the whole sequence flo ..... fit, ie maximizing the posterior density …

… in Monte Carlo-based Kalman filter variants used for NWP. Our … The best known filtering (data
assimilation) algorithm is in the … The Kalman filter recursions are crucial to our work and are …

… and explaining the linear Kalman filter algorithm, and will … Kalman filter algorithm; a version
of the linear Kalman filter which … If now we subsequently use this posterior distribution as the …

… ), the objective of the filtering is to estimate the optimal current … is in essence amount to
estimating the posterior density p(xn|… state estimation problem and included the Kalman filter as a …

… Kalman Filter (KF) that is also known as linear quadratic estimation filter estimates current
states of … process model. Thus algorithm is implemented in two steps: in the prediction step an …

… De Finetti: A robust KF model would be one for which the posterior distribution of the state of
… in precision of the estimate of the state of nature. Thus this filter neither accepts nor rejects a …

… optimal Bayesian Kalman filter, which is optimal over posterior distribution … a model-specific
Kalman filter relative to the MAP estimates of the noise parameters. To find the MAP estimates…

… dynamic model than the most commonly used algorithm for the state estimation is the Extended
Kalman Filter (… The discrete weighted approximation to the posterior pdf we can define …

… From a statistical perspective, obtaining estimates of the true … filtering, which attempts to
obtain sequentially the posterior … it avoids calculation and storage of the n × m t Kalman gain …