C. Riggelsen

发表

Model Selection in Seismic Hazard Analysis: An Information-Theoretic Perspective

F. Scherbaum, É. Delavaud, C. Riggelsen, 2009 .

Simultaneous quantification of epistemic and aleatory uncertainty in GMPEs using Gaussian process regression

Carsten Riggelsen, Marcel Hermkes, C. Riggelsen, 2014, Bulletin of Earthquake Engineering.

Flood Damage and Influencing Factors: A Bayesian Network Perspective

F. Scherbaum, C. Riggelsen, B. Merz, 2012 .

Comparisons among the five ground-motion models developed using RESORCE for the prediction of response spectral accelerations due to earthquakes in Europe and the Middle East

J. Bommer, J. Douglas, F. Scherbaum, 2014, Bulletin of Earthquake Engineering.

Unsupervised feature selection for pattern search in seismic time series

F. Scherbaum, C. Riggelsen, M. Ohrnberger, 2008 .

Quantification of epistemic uncertainties in probabilistic seismic hazard analysis

Frank Scherbaum, Matthias Ohrnberger, Carsten Riggelsen, 2011 .

Bayesian networks for tsunami early warning

Frank Scherbaum, Lilian Blaser, Matthias Ohrnberger, 2011 .

Deriving Empirical Ground-Motion Models: Balancing Data Constraints and Physical Assumptions to Optimize Prediction Capability

F. Scherbaum, C. Riggelsen, N. Kuehn, 2009 .

Approximation Methods for Efficient Learning of Bayesian Networks

Carsten Riggelsen, C. Riggelsen, 2008, Frontiers in Artificial Intelligence and Applications.

Unsupervised Feature Selection for Pattern Discovery in Seismic Wavefields

Frank Scherbaum, Matthias Ohrnberger, Carsten Riggelsen, 2008, FSDM.

How useful are complex flood damage models?

Bruno Merz, Heidi Kreibich, Frank Scherbaum, 2014 .

Dynamic Bayesian Networks for Real-Time Classification of Seismic Signals

Frank Scherbaum, Matthias Ohrnberger, Carsten Riggelsen, 2007, PKDD.

Bayesian Belief Network for Tsunami Warning Decision Support

Frank Scherbaum, Lilian Blaser, Matthias Ohrnberger, 2009, ECSQARU.

Learning Bayesian Networks: A MAP Criterion for Joint Selection of Model Structure and Parameter

Carsten Riggelsen, C. Riggelsen, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Learning Bayesian Networks from Incomplete Data: An Efficient Method for Generating Approximate Predictive Distributions

Carsten Riggelsen, C. Riggelsen, 2006, SDM.

An Interactive Tool for the Elicitation of Subjective Probabilities in Probabilistic Seismic-Hazard Analysis

Frank Scherbaum, Carsten Riggelsen, Andrew Curtis, 2013 .

Modeling the Joint Probability of Earthquake, Site, and Ground-Motion Parameters Using Bayesian Networks

Frank Scherbaum, Carsten Riggelsen, Nicolas Kuehn, 2011 .

A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations

Matthias Ohrnberger, Carsten Riggelsen, C. Riggelsen, 2014, Pure and Applied Geophysics.

Information-Theoretic Selection of Ground-Motion Prediction Equations for Seismic Hazard Analysis: An Applicability Study Using Californian Data

Frank Scherbaum, Carsten Riggelsen, Nicolas Kuehn, 2009 .

Learning parameters of Bayesian networks from incomplete data via importance sampling

Carsten Riggelsen, C. Riggelsen, 2006, Int. J. Approx. Reason..

Learning aggregations of ground-motion models using data

Frank Scherbaum, Carsten Riggelsen, Nikolaos Gianniotis, 2011 .

Visualisation of high-dimensional data using an ensemble of neural networks

Carsten Riggelsen, Nikolaos Gianniotis, C. Riggelsen, 2013, 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL).