Selected article for: "bayesian MAP estimate and prior assumed bayesian MAP estimate"
Author: Spencer Woody; Mauricio Garcia Tec; Maytal Dahan; Kelly Gaither; Spencer Fox; Lauren Ancel Meyers; James G Scott
Title: Projections for first-wave COVID-19 deaths across the US using social-distancing measures derived from mobile phones
  • Document date: 2020_4_22
    • ID: 87lxnslh_26
    Snippet: The IHME model-fitting process. We note that the IHME model parameterizes its Gaussian curves in a slightly different way, but the underlying family is identical to Equation (1), in the sense that there is a bijection between our parameterization and theirs. Briefly, the IHME model assumes that the cumulative death rate is proportional to Gaussian CDF, and they fit the three model parameters by optimizing a penalized least-squares objective on th.....
    Document: The IHME model-fitting process. We note that the IHME model parameterizes its Gaussian curves in a slightly different way, but the underlying family is identical to Equation (1), in the sense that there is a bijection between our parameterization and theirs. Briefly, the IHME model assumes that the cumulative death rate is proportional to Gaussian CDF, and they fit the three model parameters by optimizing a penalized least-squares objective on the log-cumulativedeaths scale, interpreting the result as a Bayesian maximum a posteriori (MAP) estimate under an assumed prior. More specifically, let N i be the population in state i and define r it = N −1 i ∑ s
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