Exact Bayesian curve fitting and signal segmentation.

We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem

The meaning of my feelings depends on who I am: work-related identifications shape emotion effects in organizations

Theory and research on affect in organizations has mostly approached emotions from a valence perspective, suggesting that positive emotions lead to positive outcomes and negative emotions to negative outcomes for organizations. We propose that cognition resulting from emotional experiences at work cannot be assumed based on emotion valence alone. Instead, building on appraisal theory and social identity theory, we propose that individual responses to discrete emotions in organizations are shaped by, and thus depend on, work-related identifications. We elaborate on this proposition specifically with respect to turnover intentions, theorizing how three discrete emotions - anger, guilt, and pride - differentially affect turnover intentions, depending on two work-related identifications - organizational and occupational identification. A longitudinal study involving 135 pilot instructors reporting emotions, work-related identifications, and turnover intentions over the course of one year provides general support for our proposition. Our theory and findings advance emotion and identity theories by explaining how the effects of emotions are dependent on the psychological context in which they are experienced

Crowdedness mediates the effect of social identification on positive emotion in a crowd : a survey of two crowd events

Exposure to crowding is said to be aversive, yet people also seek out and enjoy crowded situations. We surveyed participants at two crowd events to test the prediction of self-categorization theory that variable emotional responses to crowding are a function of social identification with the crowd. In data collected from participants who attended a crowded outdoor music event (n = 48), identification with the crowd predicted feeling less crowded; and there was an indirect effect of identification with the crowd on positive emotion through feeling less crowded. Identification with the crowd also moderated the relation between feeling less crowded and positive emotion. In data collected at a demonstration march (n = 112), identification with the crowd predicted central (most dense) location in the crowd; and there was an indirect effect of identification with the crowd on positive emotion through central location in the crowd. Positive emotion in the crowd also increased over the duration of the crowd event. These findings are in line with the predictions of self-categorization theory. They are inconsistent with approaches that suggest that crowding is inherently aversive; and they cannot easily be explained through the concept of ‘personal space’.Publisher PDFPeer reviewe