5 citations · 7 across the 6 of their papers we have counts for
6 papers
Asymptotic properties of Vecchia approximation for Gaussian processes
Myeongjong Kang, Florian Schäfer, Joseph Guinness +1
Vecchia approximation has been widely used to accurately scale Gaussian-process (GP) inference to large datasets, by expressing the joint density as a product of conditional densit…
Bayesian nonparametric generative modeling of large multivariate non-Gaussian spatial fields
Paul F. V. Wiemann, Matthias Katzfuss
Multivariate spatial fields are of interest in many applications, including climate model emulation. Not only can the marginal spatial fields be subject to nonstationarity, but the…
Locally anisotropic covariance functions on the sphere
Jian Cao, Jingjie Zhang, Zhuoer Sun +1
Rapid developments in satellite remote-sensing technology have enabled the collection of geospatial data on a global scale, hence increasing the need for covariance functions that…
Scalable Spatio-Temporal Smoothing via Hierarchical Sparse Cholesky Decomposition
Marcin Jurek, Matthias Katzfuss
We propose an approximation to the forward-filter-backward-sampler (FFBS) algorithm for large-scale spatio-temporal smoothing. FFBS is commonly used in Bayesian statistics when wor…
A Bayesian adaptive ensemble Kalman filter for sequential state and parameter estimation
Jonathan R. Stroud, Matthias Katzfuss, Christopher K. Wikle
This paper proposes new methodology for sequential state and parameter estimation within the ensemble Kalman filter. The method is fully Bayesian and propagates the joint posterior…
BADER: Bayesian analysis of differential expression in RNA sequencing data
Matthias Katzfuss, Andreas Neudecker, Simon Anders +1
Identifying differentially expressed genes from RNA sequencing data remains a challenging task because of the considerable uncertainties in parameter estimation and the small sampl…