2 resultados para Bayesian Inference, HIghest Posterior Density, Invariance, Odds Ratio, Objective Priors

em Boston University Digital Common


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Objective: To identify differences between manufacturing firms in Nigeria that have undertaken HIV/AIDS prevention activities and those that have not as a step toward improving the targeting of HIV policies and interventions. Methods: A survey of a representative sample of registered manufacturing firms in Nigeria, stratified by location, workforce size, and industrial sector. The survey was administered to managers of 232 firms representing most major industrial areas and sectors in March-April 2001. Results: 45.3 percent of the firms’ managers received information about HIV/AIDS from a source outside the firm in 2000; 7.7 percent knew of an employee who was HIV-positive at the time of the survey; and 13.6 percent knew of an employee who had left the firm and/or died in service due to AIDS. Only 31.7 percent of firms took any action to prevent HIV among employees in 2000, and 23.9 percent had discussed the epidemic as a potential business concern. The best correlates of having taken action on HIV were knowledge of an HIV-positive employee or having lost an employee to AIDS (odds ratio [OR] 6.36, 95% confidence interval [CI]: 2.30, 17.57) and receiving information about the disease from an outside source (OR 7.83, 95% CI: 3.46, 17.69). Conclusions: Despite a nationwide HIV seroprevalence of 5.8 percent, as of 2001 most Nigerian manufacturing firm managers did not regard HIV/AIDS as a serious problem and had neither taken any action on it nor discussed it as a business issue. Providing managers with accurate, relevant information about the epidemic and practical prevention interventions might strengthen the business response to AIDS in countries like Nigeria.

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How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.