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

This article presents comparisons among the five ground-motion models described in other articles within this special issue, in terms of data selection criteria, characteristics of the models and predicted peak ground and response spectral accelerations. Comparisons are also made with predictions from the Next Generation Attenuation (NGA) models to which the models presented here have similarities (e.g. a common master database has been used) but also differences (e.g. some models in this issue are nonparametric). As a result of the differing data selection criteria and derivation techniques the predicted median ground motions show considerable differences (up to a factor of two for certain scenarios), particularly for magnitudes and distances close to or beyond the range of the available observations. The predicted influence of style-of-faulting shows much variation among models whereas site amplification factors are more similar, with peak amplification at around 1s. These differences are greater than those among predictions from the NGA models. The models for aleatory variability (sigma), however, are similar and suggest that ground-motion variability from this region is slightly higher than that predicted by the NGA models, based primarily on data from California and Taiwan

RegNetB: Predicting Relevant Regulator-Gene Relationships in Localized Prostate Tumor Samples

<p>Abstract</p> <p>Background</p> <p>A central question in cancer biology is what changes cause a healthy cell to form a tumor. Gene expression data could provide insight into this question, but it is difficult to distinguish between a gene that causes a change in gene expression from a gene that is affected by this change. Furthermore, the proteins that regulate gene expression are often themselves not regulated at the transcriptional level. Here we propose a Bayesian modeling framework we term RegNetB that uses mechanistic information about the gene regulatory network to distinguish between factors that cause a change in expression and genes that are affected by the change. We test this framework using human gene expression data describing localized prostate cancer progression.</p> <p>Results</p> <p>The top regulatory relationships identified by RegNetB include the regulation of RLN1, RLN2, by PAX4, the regulation of ACPP (PAP) by JUN, BACH1 and BACH2, and the co-regulation of PGC and GDF15 by MAZ and TAF8. These target genes are known to participate in tumor progression, but the suggested regulatory roles of PAX4, BACH1, BACH2, MAZ and TAF8 in the process is new.</p> <p>Conclusion</p> <p>Integrating gene expression data and regulatory topologies can aid in identifying potentially causal mechanisms for observed changes in gene expression.</p

Approximation methods for efficient learning of Bayesian networks

This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work

Approximation Methods for Efficient Learning of Bayesian Networks

Learning from data ranges between extracting essentials from the data, to the more fundamental and very challenging task of learning the underlying data generating process in terms of a probability distribution. In particular, in this thesis we assume that this distribution can be modelled as a Bayesian network. In terms of interpretability, the directed graphical structure (model) of a BN is attractive, because explicit insight is gained into relationships between variables. Most methods for learning require complete data in order to work or produce valid results. Unfortunately real-life databases are rarely complete. Learning from incomplete data is a non-trivial extension of existing methods developed for learning from complete data. In this thesis we develop and investigate efficient methods for learning Bayesian networks from both complete and incomplete data with emphasis on the latter. Several issues with regard to learning of Bayesian networks are discussed, including regularisation, and various scoring metrics are investigated and derived. Special attention is paid to a Bayesian statistical approach to learning. By way of Markov chain Monte Carlo (MCMC) simulation, Bayesian analysis has become a feasible alternative to the classical statistical approach. Different MCMC algorithms are presented for learning of Bayesian networks, both parameter and model, in a Bayesian statistical context. For large amounts of incomplete data where MCMC methods tend to be inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. This leads to a very fast algorithm that outperforms existing approximate algorithms, and even competes with (structural) EM