These results suggest that the often-reported increase in sleep problems with age is a nonlinear phenomenon, mediated by factors other than physiologic aging.Īging depression epidemiology fatigue sleep quality. The pattern was similar for TIREDNESS.Īdvancing age was not associated with increased Self-Reported Sleep Disturbance or Self-Reported Tiredness/Lack of Energy. Agena.ai Modeller is a design and execution environment for creating Bayesian networks which runs on Windows, Linux and Macintosh operating systems. The pattern was similar for women, except a more marked rise was noted from age 40-59 yr. Create and deploy scaleable Bayesian network applications in the cloud Agena.ai is engineered to makes it easy to deploy AI applications in the cloud. For SLEEPDIST, odds ratios (ORs, reference = 80+) declined from age 18-54 yr, rose slightly, and then declined again after age 59 yr in men. Both SLEEPDIST and TIREDNESS generally declined across the life span, with fewest endorsements in respondents older than 80 yr. Poor general health, mild depressed mood, and moderate/severe depressed mood were associated with SLEEPDIST and TIREDNESS. Across all age groups, women reported more SLEEPDIST and TIREDNESS. All analyses were adjusted for race/ethnicity, income, education, and time since last medical checkup. Martin is CTO of Agena, who develop and distribute AgenaRisk, a software product for modelling risk and uncertainty. Predictors were age, general health, and depressed mood. Martin Neil is a Reader in Systems Risk at the Department of Computer Science, Queen Mary, University of London, where he teaches decision and risk analysis and software engineering. Outcomes were self-reported complaints in response to survey questions assessing SLEEPDIST and TIREDNESS, dichotomized as reporting a complaint < 6 versus ≥ 6 nights or days, respectively, in a 2-wk period. I have both continuous and categorical variables in my network.Explore the prevalence of sleep-related complaints across age groups, examining effects of sex, general health, and depressed mood.Ĭross-sectional analysis of data from the 2006 Behavioral Risk Factor Surveillance System (BRFSS).Ĭomplete-case analysis included 155,877 participants who responded to questions related to Self-Reported Sleep Disturbance (SLEEPDIST) and Self-Reported Tiredness/Lack of Energy (TIREDNESS). The network is certainly bigger and a little complex than the three node network here. I’m working on an implementation of Bayes Net for modeling the risk of accidents (predict likelihood of an accident). But the PyMC discourse summary that I receive in email brought my attention to this thread. We say that Martin's lateness has been 'explained away'. Thus the odds are that the train strike, rather than oversleeping, have caused Martin to be late. Entering this evidence and applying Bayes yields a revised probability of 0.54 for a train strike and 0.44 for Martin oversleeping. I was going to post a question similar to the one asked here. Now suppose we also discover that Norman is late. But hopefully I’ll get up to speed with the Bayes Net packages soon. Entering this evidence and applying Bayes yields a revised probability of 0.54 for a train strike and 0.44 for Martin oversleeping. They seem more polished, with more complete docs. It seems like there is way more development effort/people who focus on PyMC3, STAN, type frameworks than the Bayes Net packages. SmokeD = pg.DiscreteDistribution( and you can easily write down conditional probability tables. This can be implemented in pomegranate (just one of the relevant Python packages) as: import pomegranate as pg
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