General linear model (GLM) is a standard and widely used fMRI analysis tool. It enables the detection of hypothesis-driven brain activations. In contrast, Independent Component Analysis (ICA) is a powerful technique, which enables the detection of data-driven spatially independent networks. Hybrid approaches that combine and take advantage of GLM and ICA have been proposed. Yet the choice of th...

We introduce a generalized loop move (GLM) update for Monte Carlo simulations of frustrated Ising models on two-dimensional lattices with bond-sharing plaquettes. The GLM updates are designed to enhance Monte Carlo sampling efficiency when the system's low-energy states consist of an extensive number of degenerate or near-degenerate spin configurations, separated by large energy barriers to sin...

In this paper, we draw upon two sets of theoretical resources to develop a comprehensive theory of sexual offender rehabilitation named the Good Lives Model-Comprehensive (GLM-C). The original Good Lives Model (GLM-O) forms the overarching values and principles guiding clinical practice in the GLM-C. In addition, the latest sexual offender theory (i.e., the Integrated Theory of Sexual Offending...

Conventional approaches to modeling classification image data can be described in terms of a standard linear model (LM). We show how the problem can be characterized as a Generalized Linear Model (GLM) with a Bernoulli distribution. We demonstrate via simulation that this approach is more accurate in estimating the underlying template in the absence of internal noise. With increasing internal n...

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We present a flexible non-parametric generative model for multigroup regression that detects latent common clusters of groups. The model is founded on techniques that are now considered standard in the statistical parameter estimation literature, namely, Dirichlet process(DP) and Generalized Linear Model (GLM), and therefore, we name it “Infinite MultiGroup Generalized Linear Models” (iMGGLM). ...