A Bayesian scheme for fully unsupervised still image segmentation is described. The likelihood function is constructed by assuming that the grey level at each pixel site is a realization of a Gaussian random variable of unknown parameters, there being an uncertain number of distinct Gaussian classes in the image. Spatial connectivity between pixels is encouraged via a Markov random field prior....

Respondent-Driven Sampling (RDS) is used throughout the world to estimate prevalences and population sizes for hard-to-reach populations. Although RDS is an effective method for enrolling people from key populations (KPs) in studies, it relies on an unknown sampling mechanism and thus each individual’s inclusion probability is unknown. Current estimators rely on a participant’s network size (de...

Stochastic sampling based trackers have shown good performance for abrupt motion tracking so that they have gained popularity in recent years. However, conventional methods tend to use a two-stage sampling paradigm, in which the search space needs to be uniformly explored with an inefficient preliminary sampling phase. In this paper, we propose a novel sampling-based method in the Bayesian filt...

Participatory design (PD) can play an important role in obtaining benefits from healthcare information technologies, but we contend that to fulfil this role PD must incorporate feedback from real use of the technologies. In this paper we describe an effects-driven PD approach that revolves around a sustained focus on pursued effects and uses the experience sampling method (ESM) to collect real-...

Respondent-driven sampling (RDS) is a link-tracing sampling method that is especially suitable for sampling hidden populations. RDS combines an efficient snowball-type sampling scheme with inferential procedures that yield unbiased population estimates under some assumptions about the sampling procedure and population structure. Several seed individuals are typically used to initiate RDS recrui...

Respondent-Driven Sampling (RDS) is n approach to sampling design and inference in hard-to-reach human populations. It is often used in situations where the target population is rare and/or stigmatized in the larger population, so that it is prohibitively expensive to contact them through the available frames. Common examples include injecting drug users, men who have sex with men, and female s...

The adaptive cluster sampling (ACS) is a suitable sampling design for rare and clustered populations. In environmental and ecological applications, biological populations are generally animals or plants with highly patchy spatial distribution. However, ACS would be a less efficient design when the study population is not rare with low aggregation since the final sample size could be easily out ...

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a-priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced who...

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of varianc...