Markov chain Monte Carlo (MCMC) methods have been used extensively in statistical physics over the last 40 years, in spatial statistics for the past 20 and in Bayesian image analysis over the last decade. In the last five years, MCMC has been introduced into significance testing, general Bayesian inference and maximum likelihood estimation. This paper presents basic methodology of MCMC, emphasizing the Bayesian paradigm, conditional probability and the intimate relationship with Markov random fields in spatial statistics. Hastings algorithms are discussed, including Gibbs, Metropolis and some other variations. Pairwise difference priors are described and are used subsequently in three Bayesian applications, in each of which there is a pronounced spatial or temporal aspect to the modeling. The examples involve logistic regression in the presence of unobserved covariates and ordinal factors; the analysis of agricultural field experiments, with adjustment for fertility gradients; and processing of low-resolution medical images obtained by a gamma camera. Additional methodological issues arise in each of these applications and in the Appendices. The paper lays particular emphasis on the calculation of posterior probabilities and concurs with others in its view that MCMC facilitates a fundamental breakthrough in applied Bayesian modeling.

Many business process modeling techniques have been proposed over the last decades, creating a demand for theory to assist in the comparison and evaluation of these techniques. A widely established way of determining the effectiveness and efficiency of modeling techniques is by way of representational analysis. This paper comparatively assesses representational analyses of 12 popular process modeling techniques in order to provide insights into the extent to which they differ from each other. We discuss several implications of our findings. Our analysis uncovers and explores representational root causes for a number of shortcomings that remain in process modeling practice, such as lack of process decomposition and integration of business rule specification. Our findings also serve as motivation and input to future research in areas such as context-aware business process design and conventions management.

Purpose: The purpose of this study is to explore the role of accountants as advisors in professional services firms (PSFs), and it examines the impact of digital transformation on the work, knowledge and skills of accountants in their role as advisors in PSFs.Design/methodology/approach: Data were collected using semi-structured interviews predominantly with partners in Australian PSFs, including the Big4 and directors of professional accounting bodies.Findings: The findings show that accountants as advisors fill a critical role in PSFs as they represent substantial human capital for such firms. Accountants as advisors are a valuable strategic resource because of their unique capabilities in combining generic human capital with digital human capital and social capital resources. Some differences between the Big4 and non-Big4 were found in terms of services offered that were attributable to the respective industry foci and resource availability.Practical implications: The findings have broader implications for both the accounting profession and the education sector in terms of providing a better, and more overt, understanding of what the future holds for the accounting profession and the relevant knowledge and skills required. Also, recruiters and managers at PSFs are likely to benefit from the findings.Originality/value: Evidence from PSFs provides insights into an evolutionary path for the accounting profession, and the knowledge and skills accountants need to work in that increasingly competitive domain, due to digital transformation.