A review of modern computational algorithms for Bayesian optimal design

Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has the development of simulation-based design methods, which involve a number of algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayes methods, facilitating more complex design problems to be solved. The Bayesian framework provides a unified approach for incorporating prior information and/or uncertainties regarding the statistical model with a utility function which describes the experimental aims. In this paper, we provide a general overview on the concepts involved in Bayesian experimental design, and focus on describing some of the more commonly used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the Bayesian optimal design. We also discuss other computational strategies for further research in Bayesian optimal design.

Cost efficiency of Japanese steam power generation companies : a Bayesian comparison of random and fixed frontier models

This study analyses and compares the cost efficiency of Japanese steam power generation companies using the fixed and random Bayesian frontier models. We show that it is essential to account for heterogeneity in modelling the performance of energy companies. Results from the model estimation also indicate that restricting CO2 emissions can lead to a decrease in total cost. The study finally discusses the efficiency variations between the energy companies under analysis, and elaborates on the managerial and policy implications of the results.

Bayesian models for spatio-temporal assessment of disease

This thesis has contributed to the advancement of knowledge in disease modelling by addressing interesting and crucial issues relevant to modelling health data over space and time. The research has led to the increased understanding of spatial scales, temporal scales, and spatial smoothing for modelling diseases, in terms of their methodology and applications. This research is of particular significance to researchers seeking to employ statistical modelling techniques over space and time in various disciplines. A broad class of statistical models are employed to assess what impact of spatial and temporal scales have on simulated and real data.

An investigation of the impact of various geographical scales for the specification of spatial dependence

Ecological studies are based on characteristics of groups of individuals, which are common in various disciplines including epidemiology. It is of great interest for epidemiologists to study the geographical variation of a disease by accounting for the positive spatial dependence between neighbouring areas. However, the choice of scale of the spatial correlation requires much attention. In view of a lack of studies in this area, this study aims to investigate the impact of differing definitions of geographical scales using a multilevel model. We propose a new approach -- the grid-based partitions and compare it with the popular census region approach. Unexplained geographical variation is accounted for via area-specific unstructured random effects and spatially structured random effects specified as an intrinsic conditional autoregressive process. Using grid-based modelling of random effects in contrast to the census region approach, we illustrate conditions where improvements are observed in the estimation of the linear predictor, random effects, parameters, and the identification of the distribution of residual risk and the aggregate risk in a study region. The study has found that grid-based modelling is a valuable approach for spatially sparse data while the SLA-based and grid-based approaches perform equally well for spatially dense data.

Performance Measurement in Perspective: An exploratory study of Australian foundations’ approaches to performance measurement

Performance measurement in Australian philanthropic foundations is a hot topic. Foundation staff and board members are concerned with striking the right balance between their need for information with which to assess the effectiveness of their grant-making programs, and the costs in both time and money for grantees. Influenced by normative pressures, the increasing size and professionalism of the Australian philanthropic sector, and trends from the U.S.A and the U.K, foundations are talking amongst themselves, seeking expert advice and training, consulting with grantees and trying different approaches. Many resources examine methods of data collection, measurement or analysis. Our study instead treads into less charted but important territory: the motivations and values that are shaping the debate about performance measurement. In a series of 40 interviews with foundations from Queensland, New South Wales, Victoria and South Australia, we asked whether they felt under pressure to measure performance and if so, why. We queried whether everyone in the foundation shared the same views on the purposes of performance measurement; and the ways in which the act of performance measurement changed their grant-making, their attitude to risk, their relationship with grantees and their collaborations with other funders. Unsurprisingly, a very diverse set of approaches to performance measurement were revealed.

Interpreting scratch assays using pair density dynamics and approximate Bayesian computation

Quantifying the impact of biochemical compounds on collective cell spreading is an essential element of drug design, with various applications including developing treatments for chronic wounds and cancer. Scratch assays are a technically simple and inexpensive method used to study collective cell spreading; however, most previous interpretations of scratch assays are qualitative and do not provide estimates of the cell diffusivity, D, or the cell proliferation rate,l. Estimating D and l is important for investigating the efficacy of a potential treatment and provides insight into the mechanism through which the potential treatment acts. While a few methods for estimating D and l have been proposed, these previous methods lead to point estimates of D and l, and provide no insight into the uncertainty in these estimates. Here, we compare various types of information that can be extracted from images of a scratch assay, and quantify D and l using discrete computational simulations and approximate Bayesian computation. We show that it is possible to robustly recover estimates of D and l from synthetic data, as well as a new set of experimental data. For the first time, our approach also provides a method to estimate the uncertainty in our estimates of D and l. We anticipate that our approach can be generalized to deal with more realistic experimental scenarios in which we are interested in estimating D and l, as well as additional relevant parameters such as the strength of cell-to-cell adhesion or the strength of cell-to-substrate adhesion.

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1–28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

Information experience : approaches to theory and practice

The Queensland University of Technology Research Team has recently edited a book on the idea of information experience. Authors from many parts of the world and focussed on many contexts have contributed chapters. The book represents the first attempt to profile and discuss information experience as a research domain and a research object, together with emphasis on different theories and methods being used in workplace, educational and community contexts. In this session members of the editorial team will introduce the book. We anticipate it will be of interest to any information professionals, students or researchers interested in understanding peoples' information experience.

Exploring the usefulness of structural–functional approaches to analyse governance of planning systems

Existing planning theories tend to be limited in their analytical scope and often fail to account for the impact of many interactions between the multitudes of stakeholders involved in strategic planning processes. Although many theorists rejected structural–functional approaches from the 1970s, this article argues that many of structural–functional concepts remain relevant and useful to planning practitioners. In fact, structural–functional approaches are highly useful and practical when used as a foundation for systemic analysis of real-world, multi-layered, complex planning systems to support evidence-based governance reform. Such approaches provide a logical and systematic approach to the analysis of the wider governance of strategic planning systems that is grounded in systems theory and complementary to existing theories of complexity and planning. While we do not propose its use as a grand theory of planning, this article discusses how structural–functional concepts and approaches might be applied to underpin a practical analysis of the complex decision-making arrangements that drive planning practice, and to provide the evidence needed to target reform of poorly performing arrangements.