Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

The AMP Markov property is a recently proposed alternative Markov property for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced LWF Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.Comment: 15 pages, article will appear in Scandinavian Journal of Statistic

Challenges for Efficient Query Evaluation on Structured Probabilistic Data

Query answering over probabilistic data is an important task but is generally intractable. However, a new approach for this problem has recently been proposed, based on structural decompositions of input databases, following, e.g., tree decompositions. This paper presents a vision for a database management system for probabilistic data built following this structural approach. We review our existing and ongoing work on this topic and highlight many theoretical and practical challenges that remain to be addressed.Comment: 9 pages, 1 figure, 23 references. Accepted for publication at SUM 201

Stable isotope records for the last 10 000 years from Okshola cave (Fauske, northern Norway) and regional comparisons

The sensitivity of terrestrial environments to past changes in heat transport is expected to be manifested in Holocene climate proxy records on millennial to seasonal timescales. Stalagmite formation in the Okshola cave near Fauske (northern Norway) began at about 10.4 ka, soon after the valley was deglaciated. Past monitoring of the cave and surface has revealed stable modern conditions with uniform drip rates, relative humidity and temperature. Stable isotope records from two stalagmites provide time-series spanning from c. 10 380 yr to AD 1997; a banded, multi-coloured stalagmite (Oks82) was formed between 10 380 yr and 5050 yr, whereas a pristine, white stalagmite (FM3) covers the period from ~7500 yr to the present. The stable oxygen isotope (δ<sup>18</sup>O<sub>c</sub>), stable carbon isotope (δ<sup>13</sup>C<sub>c</sub>), and growth rate records are interpreted as showing i) a negative correlation between cave/surface temperature and δ<sup>18</sup>O<sub>c</sub>, ii) a positive correlation between wetness and δ<sup>13</sup>C<sub>c</sub>, and iii) a positive correlation between temperature and growth rate. Following this, the data from Okshola show that the Holocene was characterised by high-variability climate in the early part, low-variability climate in the middle part, and high-variability climate and shifts between two distinct modes in the late part. <br><br> A total of nine Scandinavian stalagmite δ<sup>18</sup>O<sub>c</sub> records of comparable dating precision are now available for parts or most of the Holocene. None of them show a clear Holocene thermal optimum, suggesting that they are influenced by annual mean temperature (cave temperature) rather than seasonal temperature. For the last 1000 years, δ<sup>18</sup>O<sub>c</sub> values display a depletion-enrichment-depletion pattern commonly interpreted as reflecting the conventional view on climate development for the last millennium. Although the δ<sup>18</sup>O<sub>c</sub> records show similar patterns and amplitudes of change, the main challenges for utilising high-latitude stalagmites as palaeoclimate archives are i) the accuracy of the age models, ii) the ambiguity of the proxy signals, and iii) calibration with monitoring data

Effect of catalyst layer defects on local membrane degradation in polymer electrolyte fuel cells

© 2016 Elsevier B.V. All rights reserved. Aiming at durability issues of fuel cells, this research is dedicated to a novel experimental approach in the analysis of local membrane degradation phenomena in polymer electrolyte fuel cells, shedding light on the potential effects of manufacturing imperfections on this process. With a comprehensive review on historical failure analysis data from field operated fuel cells, local sources of iron oxide contaminants, catalyst layer cracks, and catalyst layer delamination are considered as potential candidates for initiating or accelerating the local membrane degradation phenomena. Customized membrane electrode assemblies with artificial defects are designed, fabricated, and subjected to membrane accelerated stress tests followed by extensive post-mortem analysis. The results reveal a significant accelerating effect of iron oxide contamination on the global chemical degradation of the membrane, but dismiss local traces of iron oxide as a potential stressor for local membrane degradation. Anode and cathode catalyst layer cracks are observed to have negligible impact on the membrane degradation phenomena. Notably however, distinct evidence is found that anode catalyst layer delamination can accelerate local membrane thinning, while cathode delamination has no apparent effect. Moreover, a substantial mitigating effect for platinum residuals on the site of delamination is observed

Microscopic Origin of Quantum Chaos in Rotational Damping

The rotational spectrum of $^{168}$Yb is calculated diagonalizing different effective interactions within the basis of unperturbed rotational bands provided by the cranked shell model. A transition between order and chaos taking place in the energy region between 1 and 2 MeV above the yrast line is observed, associated with the onset of rotational damping. It can be related to the higher multipole components of the force acting among the unperturbed rotational bands.Comment: 7 pages, plain TEX, YITP/K-99

Moderated Network Models

Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research this is often implausible. In this paper, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct the MNM and propose an L1-regularized nodewise regression approach to estimate it. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification