Approximate Bayesian Computation for astronomical model analysis : a case study in galaxy demographics and morphological transformation at high redshift

Approximate Bayesian Computation’ (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the observed data under an arbitrary set of input parameters may be entirely intractable – the latter condition rendering useless the standard machinery of tractable likelihood-based, Bayesian statistical inference [e.g. conventional Markov chain Monte Carlo (MCMC) simulation]. In this paper, we demonstrate the potential of ABC for astronomical model analysis by application to a case study in the morphological transformation of high-redshift galaxies. To this end, we develop, first, a stochastic model for the competing processes of merging and secular evolution in the early Universe, and secondly, through an ABC-based comparison against the observed demographics of massive (Mgal > 1011 M⊙) galaxies (at 1.5 < z < 3) in the Cosmic Assembly Near-IR Deep Extragalatic Legacy Survey (CANDELS)/Extended Groth Strip (EGS) data set we derive posterior probability densities for the key parameters of this model. The ‘Sequential Monte Carlo’ implementation of ABC exhibited herein, featuring both a self-generating target sequence and self-refining MCMC kernel, is amongst the most efficient of contemporary approaches to this important statistical algorithm. We highlight as well through our chosen case study the value of careful summary statistic selection, and demonstrate two modern strategies for assessment and optimization in this regard. Ultimately, our ABC analysis of the high-redshift morphological mix returns tight constraints on the evolving merger rate in the early Universe and favours major merging (with disc survival or rapid reformation) over secular evolution as the mechanism most responsible for building up the first generation of bulges in early-type discs.

Building resilient communities : creating effective multi-channel communication during disaster response and recovery

Aim: To examine evidence-based strategies that motivate appropriate action and increase informed decision-making during the response and recovery phases of disasters.

Reflexivity and aesthetic inquiry : building dialogues between the arts and literacy

Reflection can form the basis for powerful dialogue between the arts and literacy as we seek interpretive and expressive fluency across modes. Through deep, cumulative reflection we make aspects of our world and experiences more perceivable, and open them up for artistic expression and aesthetic inquiry. Such reflections are also the catalysts for self-awareness and identity building. Theories of reflexivity offer a useful lens with which to understand our relationship with the world and the people, texts and things within it. The reflexive process can prompt us to challenge our understandings and change our representations of self and others through text. This paper offers a discussion of reflexivity and the ways in which it can be expressed and performed in discursive and non-discursive ways to develop literacies through and in the arts.

Advanced computer simulation of the milling process

Process modelling is an integral part of any process industry. Several sugar factory models have been developed over the years to simulate the unit operations. An enhanced and comprehensive milling process simulation model has been developed to analyse the performance of the milling train and to assess the impact of changes and advanced control options for improved operational efficiency. The developed model is incorporated in a proprietary software package ‘SysCAD’. As an example, the milling process model has been used to predict a significant loss of extraction by returning the cush from the juice screen before #3 mill instead of before #2 mill as is more commonly done. Further work is being undertaken to more accurately model extraction processes in a milling train, to examine extraction issues dynamically and to integrate the model into a whole factory model.

Simulation-based density estimation for time series using covariate data

This paper proposes a simulation-based density estimation technique for time series that exploits information found in covariate data. The method can be paired with a large range of parametric models used in time series estimation. We derive asymptotic properties of the estimator and illustrate attractive finite sample properties for a range of well-known econometric and financial applications.

The building blocks of digital media literacy: Socio-material participation and the production of media knowledge

This article outlines the knowledge and skills students develop when they engage in digital media production and analysis in school settings. The metaphor of ‘digital building blocks’ is used to describe the material practices, conceptual understandings and production of knowledge that lead to the development of digital media literacy. The article argues that the two established approaches to media literacy education, critical reading and media production, do not adequately explain how students develop media knowledge. It suggests there has been too little focus on material practices and how these relate to the development of conceptual understanding in media learning. The article explores empirical evidence from a four-year investigation in a primary school in Queensland, Australia using actor–network theory to explore ‘moments of translation’ as students deploy technologies and concepts to materially participate in digital culture. A generative model of media learning is presented with four categories of building blocks that isolate the specific skills and knowledge that can be taught and learnt to promote participation in digital media contexts: digital materials, conceptual understandings, media production and media analysis. The final section of the article makes initial comments on how the model might become the basis for curriculum development in schools and argues that further empirical research needs to occur to confirm the model’s utility.