TY  - JOUR
A1  - Gianniotis, Nikolaos
A1  - Kuehn, Nicolas
A1  - Scherbaum, Frank
T1  - Manifold aligned ground motion prediction equations for regional datasets
JF  - Computers & geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology
N2  - Inferring a ground-motion prediction equation (GMPE) for a region in which only a small number of seismic events has been observed is a challenging task. A response to this data scarcity is to utilise data from other regions in the hope that there exist common patterns in the generation of ground motion that can contribute to the development of a GMPE for the region in question. This is not an unreasonable course of action since we expect regional GMPEs to be related to each other. In this work we model this relatedness by assuming that the regional GMPEs occupy a common low-dimensional manifold in the space of all possible GMPEs. As a consequence, the GMPEs are fitted in a joint manner and not independent of each other, borrowing predictive strength from each other's regional datasets. Experimentation on a real dataset shows that the manifold assumption displays better predictive performance over fitting regional GMPEs independent of each other. (C) 2014 Elsevier Ltd. All rights reserved.
KW  - Ground-motion-model
KW  - Bagging
KW  - Ensembles
KW  - Manifold
KW  - Regional-dependence
Y1  - 2014
U6  - https://doi.org/10.1016/j.cageo.2014.04.014
SN  - 0098-3004
SN  - 1873-7803
VL  - 69
SP  - 72
EP  - 77
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Gianniotis, Nikolaos
A1  - Schnoerr, Christoph
A1  - Molkenthin, Christian
A1  - Bora, Sanjay Singh
T1  - Approximate variational inference based on a finite sample of Gaussian latent variables
JF  - Pattern Analysis & Applications
N2  - Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.
KW  - Bayesian inference
KW  - Posterior estimation
KW  - Expectation maximisation
Y1  - 2016
U6  - https://doi.org/10.1007/s10044-015-0496-9
SN  - 1433-7541
SN  - 1433-755X
VL  - 19
SP  - 475
EP  - 485
PB  - Springer
CY  - New York
ER  -