183 citations · 265 across the 5 of their papers we have counts for
6 papers
Simultaneous estimation of the effective reproduction number and the time series of daily infections: Application to Covid-19
Hans R. Künsch, Fabio Sigrist
The time-varying effective reproduction number is an important parameter for communication and policy decisions during an epidemic. In this paper, we present new statistical method…
Particle filters for high-dimensional geoscience applications: a review
Peter Jan van Leeuwen, Hans R. Künsch, Lars Nerger +2
Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, but their application to the geosciences has been limit…
Particle Filters and Data Assimilation
Paul Fearnhead, Hans Künsch
State-space models can be used to incorporate subject knowledge on the underlying dynamics of a time series by the introduction of a latent Markov state-process. A user can specify…
A Conversation with Peter Huber
Andreas Buja, Hans R. Künsch
Peter J. Huber was born on March 25, 1934, in Wohlen, a small town in the Swiss countryside. He obtained a diploma in mathematics in 1958 and a Ph.D. in mathematics in 1961, both f…
Comment: The 2005 Neyman Lecture: Dynamic Indeterminism in Science
Hans R. Künsch
Comment on ``The 2005 Neyman Lecture: Dynamic Indeterminism in Science'' [arXiv:0808.0620]
Recursive Monte Carlo filters: Algorithms and theoretical analysis
Hans R. Künsch
Recursive Monte Carlo filters, also called particle filters, are a powerful tool to perform computations in general state space models. We discuss and compare the accept--reject ve…