Properties of the bridge sampler with a focus on splitting the MCMC sample

Computation of normalizing constants is a fundamental mathematical problem in various disciplines, particularly in Bayesian model selection problems. A sampling-based technique known as bridge sampling (Meng and Wong in Stat Sin 6(4):831–860, 1996) has been found to produce accurate estimates of normalizing constants and is shown to possess good asymptotic properties. For small to moderate sample sizes (as in situations with limited computational resources), we demonstrate that the (optimal) bridge sampler produces biased estimates. Specifically, when one density (we denote as $$p_2$$) is constructed to be close to the target density (we denote as $$p_1$$) using method of moments, our simulation-based results indicate that the correlation-induced bias through the moment-matching procedure is non-negligible. More crucially, the bias amplifies as the dimensionality of the problem increases. Thus, a series of theoretical as well as empirical investigations is carried out to identify the nature and origin of the bias. We then examine the effect of sample size allocation on the accuracy of bridge sampling estimates and discovered that one possibility of reducing both the bias and standard error with a small increase in computational effort is by drawing extra samples from the moment-matched density $$p_2$$ (which we assume easy to sample from), provided that the evaluation of $$p_1$$ is not too expensive. We proceed to show how the simple adaptive approach we termed “splitting” manages to alleviate the correlation-induced bias at the expense of a higher standard error, irrespective of the dimensionality involved. We also slightly modified the strategy suggested by Wang et al. (Warp bridge sampling: the next generation, Preprint, 2019. arXiv:1609.07690) to address the issue of the increase in standard error due to splitting, which is later generalized to further improve the efficiency. We conclude the paper by offering our insights of the application of a combination of these adaptive methods to improve the accuracy of bridge sampling estimates in Bayesian applications (where posterior samples are typically expensive to generate) based on the preceding investigations, with an application to a practical example.     

Volunteering Hierarchies in the Global South: Remuneration and Livelihoods

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This paper explores volunteering and inequality in the global South through an analysis of volunteering remuneration. We...     

Human perceptions of an avian predator in an urban ecosystem: close proximity to nests increases fondness among local residents

Urban Ecosystems - How humans view their local birds influences the habitat quality for the birds. Humans that enjoy birds will often consider them when altering landscapes or may enhance their own...     

Exact estimation of multiple directed acyclic graphs

This paper considers structure learning for multiple related directed acyclic graph (DAG) models. Building on recent developments in exact estimation of DAGs using integer linear programming (ILP), we present an ILP approach for joint estimation over multiple DAGs. Unlike previous work, we do not require that the vertices in each DAG share a common ordering. Furthermore, we allow for (potentially unknown) dependency structure between the DAGs. Results are presented on both simulated data and fMRI data obtained from multiple subjects.     

Queer Blindfolding: A Case Study on Difference “Blindness” Toward Persons Who Identify as Lesbian,Gay, Bisexual,and Transgender

The purpose of this article is to introduce and explore the narrative strategy of queer blindfolding. Utilizing psycho-discursive qualitative methodology, the authors will draw from a case study to demonstrate how some beneficent, well-intended persons who identify as heterosexual adopt the narrative strategy of queer blindfolding as they negotiate the discourse of heteronormativity. We will map this narrative strategy, compare and contrast it to racial colorblindness, and unpack the accompanying intra-psychic conflict and defense mechanisms that are utilized by the participant in the case study. We will also demonstrate how this discursive strategy positions participants within systemic heterosexism.     

Bullying and 'Theory of Mind': A Critique of the 'Social Skills Deficit' View of Anti-Social Behaviour

Bullying in schools has been found to be widespread. The popular stereotype of a bully, supported by theories based on the social skills deficit model, is of a powerful but 'oafish' person with little understanding of others. In this article, we trace the origin of this view, and present an alternative view: that some bullies, at least, will need good social cognition and theory of mind skills in order to manipulate and organise others, inflicting suffering in subtle and damaging ways while avoiding detection themselves. Such skills, although likely to be utilised in all bullying, may be particularly useful for ringleader bullies and in the indirect forms of bullying which are more common between girls. Suggestions for further research in this area are made, and implications for anti-bullying work briefly discussed.