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pro vyhledávání: '"Künsch, Hans R"'
Autor:
Aeberhard, William H., Cantoni, Eva, Field, Chris, Kuensch, Hans R., Flemming, Joanna Mills, Xu, Ximing
State space models (SSMs) are now ubiquitous in many fields and increasingly complicated with observed and unobserved variables often interacting in non-linear fashions. The crucial task of validating model assumptions thus becomes difficult, particu
Externí odkaz:
http://arxiv.org/abs/2004.05023
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 limited due to their inefficiency in high-dimensional systems in standard s
Externí odkaz:
http://arxiv.org/abs/1807.10434
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 conditions by an ensemble which incorporates information coming from
Externí odkaz:
http://arxiv.org/abs/1705.02786
Autor:
Künsch, Hans R1 (AUTHOR) kuensch@stat.math.ethz.ch, Sigrist, Fabio1,2 (AUTHOR)
Publikováno v:
Journal of the Royal Statistical Society: Series A (Statistics in Society). Oct2023, Vol. 186 Issue 4, p647-648. 58p.
Autor:
Robert, Sylvain, Künsch, Hans R.
The high dimensionality and computational constraints associated with filtering problems in large-scale geophysical applications are particularly challenging for the Particle Filter (PF). Approximate but efficient methods such as the Ensemble Kalman
Externí odkaz:
http://arxiv.org/abs/1610.03701
Autor:
Robert, Sylvain, Künsch, Hans R.
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 prediction (NWP). There is a growing interest for physical models with highe
Externí odkaz:
http://arxiv.org/abs/1605.05476
Autor:
Sørland, Silje Lund, Fischer, Andreas M., Kotlarski, Sven, Künsch, Hans R., Liniger, Mark A., Rajczak, Jan, Schär, Christoph, Spirig, Curdin, Strassmann, Kuno, Knutti, Reto
Publikováno v:
In Climate Services December 2020 20
Autor:
Künsch, Hans R.
Publikováno v:
Bernoulli 2013, Vol. 19, No. 4, 1391-1403
This is a short review of Monte Carlo methods for approximating filter distributions in state space models. The basic algorithm and different strategies to reduce imbalance of the weights are discussed. Finally, methods for more difficult problems li
Externí odkaz:
http://arxiv.org/abs/1309.7807
Publikováno v:
Stat. Comput. 25, 1217 (2015)
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 particle ABC, an ensemble of particles in the product space of model outp
Externí odkaz:
http://arxiv.org/abs/1208.2157
Autor:
Frei, Marco, Künsch, Hans R.
Publikováno v:
This is an un-refereed author version of an article published in: Biometrika (2013), Volume 100, Issue 4, pages 781-800
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 update step becomes crucial. Particle filtering suffers from the well-know
Externí odkaz:
http://arxiv.org/abs/1208.0463