Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract

Cognitive Vulnerability to Depression in Canadian and Chinese Adolescents

The goal of the current study was to compare diathesis-stress and transactional models of cognitive vulnerability to depression in samples of Canadian (n = 118) and Chinese (n = 405) adolescents. We utilized a six-month multi-wave, longitudinal design in order to examine whether (a) perceived control moderated the association between the occurrence of dependent interpersonal stressors and subsequent increases in depressive symptoms (i.e., a diathesis-stress perspective) and (b) dependent interpersonal stressors mediated the association between perceived control and subsequent increases in depressive symptoms (i.e., a transactional perspective). Results from idiographic, time-lagged, hierarchical linear modeling analyses indicated that for Canadian adolescents both diathesis-stress and transactional models were significant predictors of depressive symptomology. When examining the diathesis-stress model, boys, but not girls, who reported lower perceived control, reported higher levels of depressive symptoms following the occurrence of dependent interpersonal stress. Gender differences, however, were not present in the transactional model. In contrast, transactional, but not diathesis-stress, models were significant in Chinese adolescents, and gender differences did not emerge. Overall, these results may reflect culturally-relevant differences in the etiology of depression in Canadian and Chinese adolescents

Stress generation in depression: Three studies on its resilience, possible mechanism, and symptom specificity

Three longitudinal studies examined several issues related to stress generation in depressive symptoms among undergraduates, with emphasis on mechanisms of stress generation. Study 1 replicated the stress generation effect reported in past research. Study 2 replicated Study 1's findings and, furthermore, supported the symptom specificity of stress generation to depressive versus anxious symptoms, and, perhaps most important, found that increases in hopelessness fully accounted for the stress generation finding, raising the possibility that depressive symptoms generate the perception but not the occurrence of stress. Study 3 addressed this possibility and rejected it in favor of the view that hopelessness may be a key aspect of depression in driving the generation of actual stress