94 citations · 364 across the 9 of their papers we have counts for
9 papers
A local ensemble transform Kalman particle filter for convective scale data assimilation
Sylvain Robert, Daniel Leuenberger, Hans R. Künsch
Ensemble data assimilation methods such as the Ensemble Kalman Filter (EnKF) are a key component of probabilistic weather forecasting. They represent the uncertainty in the initial…
Localization in High-Dimensional Monte Carlo Filtering
Sylvain Robert, Hans R. Künsch
The high dimensionality and computational constraints associated with filtering problems in large-scale geophysical applications are particularly challenging for the Particle Filte…
Localizing the Ensemble Kalman Particle Filter
Sylvain Robert, Hans R. Künsch
Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in large-scale geophysical applications, as for example in numerical weather predic…
A Simulated Annealing Approach to Approximate Bayes Computations
Carlo Albert, Hans R. Kuensch, Andreas Scheidegger
Approximate Bayes Computations (ABC) are used for parameter inference when the likelihood function of the model is expensive to evaluate but relatively cheap to sample from. In par…
Bridging the ensemble Kalman and particle filter
Marco Frei, Hans R. Künsch
In many applications of Monte Carlo nonlinear filtering, the propagation step is computationally expensive, and hence, the sample size is limited. With small sample sizes, the upda…
Stochastic partial differential equation based modelling of large space-time data sets
Fabio Sigrist, Hans R. Künsch, Werner A. Stahel
Increasingly larger data sets of processes in space and time ask for statistical models and methods that can cope with such data. We show that the solution of a stochastic advectio…
A dynamic nonstationary spatio-temporal model for short term prediction of precipitation
Fabio Sigrist, Hans R. Künsch, Werner A. Stahel
Precipitation is a complex physical process that varies in space and time. Predictions and interpolations at unobserved times and/or locations help to solve important problems in m…
Approximate variances for tapered spectral estimates
Michael Amrein, Hans R. Künsch
We propose an approximation of the asymptotic variance that removes a certain discontinuity in the usual formula for the raw and the smoothed periodogram in case a data taper is us…
Rate estimation in partially observed Markov jump processes with measurement errors
Michael Amrein, Hans R. Kuensch
We present a simulation methodology for Bayesian estimation of rate parameters in Markov jump processes arising for example in stochastic kinetic models. To handle the problem of m…