Browsing by Subject "Bayesian melding"

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    Assessing and integrating uncertainty into land-use forecasting
    (Journal of Transport and Land Use, 2015) Ševčíková, Hana; Simonson, Mark; Jensen, Michael
    Uncertainty in land use and transportation modeling has received increasing attention in the past few years. However, methods for quantifying uncertainty in such models are usually developed in an academic environment and in most cases do not reach users of official forecasts, such as planners and policymakers. In this paper, we describe the practical application of a methodology called Bayesian melding and its integration into the land-use forecast published by the Puget Sound Regional Council, a metropolitan planning organization. The method allows practitioners to assess uncertainty about forecasted quantities, such as households, population, and jobs, for each geographic unit. Users are provided with probability intervals around forecasts, which add value to model validation, scenario comparison, and external review and comment procedures. Practical issues such as how many runs to use or assessing uncertainty for aggregated regions are also discussed.
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    Process-based Bayesian melding of occupational exposure models and industrial workplace data
    (2012-09) Monteiro, Joao Vitor Dias
    In industrial hygiene a worker's exposure to chemical, physical and biological agents is increasingly being modeled using deterministic physical models. However, predicting exposure in real workplace settings is challenging and approaches that simply regress on a physical model (e.g. straightforward non-linear regression) are less effective as they do not account for biases attributable, at least in part, to extraneous variability. This also impairs predictive performance. We recognize these limitations and provide a rich and flexible Bayesian hierarchical framework, which we call process-based Bayesian melding (PBBM), to synthesize the physical model with the field data. We reckon that the physical model, by itself, is inadequate for enhanced inferential performance and deploy (multivariate) Gaussian processes to capture extraneous uncertainties and underlying associations. We propose rich covariance structures for multiple outcomes using latent stochastic processes. We also pay attention to computational feasibility. In particular, we explore Markov chain Monte Carlo (MCMC) as well as Integrated Nested Laplace Approximation (INLA) to estimate PBBM parameters.