A BAYESIAN ANALYSIS OF THE AGES OF FOUR OPEN CLUSTERS

In this paper we apply a Bayesian technique to determine the best fit of stellar evolution models to find the main sequence turn off age and other cluster parameters of four intermediate-age open clusters: NGC 2360, NGC 2477, NGC 2660, and NGC 3960. Our algorithm utilizes a Markov chain Monte Carlo technique to fit these various parameters, objectively finding the best fit isochrone for each cluster. The result is a high precision isochrone fit. We compare these results with the those of traditional “by eye” isochrone fitting methods. By applying this Bayesian technique to NGC 2360, NGC 2477, NGC 2660, and NGC 3960 we determine the ages of these clusters to be 1.35 ± 0.05, 1.02 ± 0.02, 1.64 ± 0.04, and 0.860 ± 0.04 Gyr, respectively. The results of this paper continue our effort to determine cluster ages to higher precision than that offered by these traditional methods of isochrone fitting

Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Organisationskultur. Eine Konkretisierung aus systemtheoretischer Perspektive

Kühl S. Organisationskultur. Eine Konkretisierung aus systemtheoretischer Perspektive. Managementforschung. 2018;28(1):7-35.Die Bestimmung des Verhältnisses von Informalität und Organisationskultur bereitet in der Organisationstheorie Schwierigkeiten. Das liegt daran, dass der Begriff Informalität häufig stillschweigend durch den Begriff der Organisationskultur ersetzt wurde, ohne dass dafür eine präzise, abgrenzungsscharfe Definition vorgenommen worden wäre. Unter Rückgriff auf Überlegungen von Dario Rodríguez argumentiert dieser Artikel, dass die beiden Begriffe Organisationskultur und Informalität das gleiche Phänomen bezeichnen: die nichtentschiedenen Entscheidungsprämissen einer Organisation. Dabei wird systematisch zwischen „unentscheidbaren Entscheidungsprämissen“ und „prinzipiell entscheidbaren, aber nicht entschiedenen Entscheidungsprämissen“ unterschieden. Es wird gezeigt, wie sich mit einer präzisen Bestimmung über das Konzept der Entscheidungsprämissen Ordnung in die „wilden Merkmallisten“ der Literatur sowohl über Informalität als auch Organisationskultur bringen lässt und empirische Phänomene genauer erfasst werden können

The ACS Survey of Galactic Globular Clusters XIV: Bayesian Single-Population Analysis of 69 Globular Clusters

We use Hubble Space Telescope (HST) imaging from the ACS Treasury Survey to determine fits for single population isochrones of 69 Galactic globular clusters. Using robust Bayesian analysis techniques, we simultaneously determine ages, distances, absorptions and helium values for each cluster under the scenario of a ‘single’ stellar population on model grids with solar ratio heavy element abundances. The set of cluster parameters is determined in a consistent and reproducible manner for all clusters using the Bayesian analysis suite BASE-9. Our results are used to re-visit the age–metallicity relation. We find correlations with helium and several other parameters such as metallicity, binary fraction and proxies for cluster mass. The helium abundances of the clusters are also considered in the context of carbon, nitrogen, and oxygen abundances and the multiple population scenario