3 Supplements for the analysis of the Dimas et al. data set 5 3.1 Choice of the grid for the average effect size . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2 Hierarchical model fed with Bayes Factors from residuals . . . . . . . . . . . . . . . . . . 6 3.3 Configuration proportions from all genes without removing expression PCs . . . . . . . . 7 3.4 Running times . . . . . . . . . ...

After viewing a scene, individuals differ in what they prioritise and remember. Culture may be one factor that influences scene memory, as Westerners have been shown to be more item-focused than Easterners (see Masuda, T., & Nisbett, R. E. (2001). Attending holistically versus analytically: Comparing the context sensitivity of Japanese and Americans. Journal of Personality and Social Psychology...

The present study assesses the effects of demographic risk factors on children's exposure to violence (ETV) and how these effects vary by informants. Data on exposure to violence of 9-, 12-, and 15-year-olds were collected from both child participants (N = 1880) and parents (N = 1776), as part of the assessment of the Project on Human Development in Chicago Neighborhoods (PHDCN). A two-level hi...

We present an hierarchical Bayes approach to modeling parameter heterogeneity in generalized linear models. The model assumes that there are relevant subpopulations and that within each subpopulation the individual-level regression coefficients have a multivariate normal distribution. However, class membership is not known a priori, so the heterogeneity in the regression coefficients becomes a ...

In this paper we propose a denoising methodology in the wavelet domain based on a Bayesian hierarchical model using Double Weibull prior. We propose two estimators, one based on posterior mean (DWWS ) and the other based on larger posterior mode (DWWSLPM ), and show how to calculate them efficiently. Traditionally, mixture priors have been used for modeling sparse wavelet coefficients. The inte...

Here we obtain approximate Bayes inferences through variational methods when an exponential power family type prior is specified for the regression coefficients to mimic the characteristics of the Bridge regression. We accomplish this through hierarchical modeling of such priors. Although the mixing distribution is not explicitly stated for scale normal mixtures, we obtain the required moments ...

Probabilistic techniques are central to data analysis, but different approaches can be challenging to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include discriminative...

In Bayesian hierarchical modeling, it is often appealing to allow the conditional density of an (observable or unobservable) random variable Y to change flexibly with categorical and continuous predictors X. A mixture of regression models is proposed, with the mixture distribution varying with X. Treating the smoothing parameters and number of mixture components as unknown, the MLE does not exi...

Typical analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long history of their successful use. However, modeling of covert, terrorist or criminal networks through social graph dose not really provide the hierarchical structure of such networks because these networks are composed of leaders and followers. It is possible m...

Whenever parameter estimates are uncertain or observations are contaminated by measurement error, the Pearson correlation coefficient can severely underestimate the true strength of an association. Various approaches exist for inferring the correlation in the presence of estimation uncertainty and measurement error, but none are routinely applied in psychological research. Here we focus on a Ba...