Selected article for: "likelihood estimation and maximum likelihood estimation"
Author: Wayne M. Getz; Richard Salter; Oliver Muellerklein; Hyun S. Yoon; Krti Tallam
Title: Modeling Epidemics: A Primer and Numerus Software Implementation
  • Document date: 2017_9_22
    • ID: 6riyqn4k_85
    Snippet: The two dominant approaches to fitting models to data are least-squares estimation (LSE), which has been largely supplanted by the maximum likelihood estimation (MLE) [54] . The latter is typically embedded in a Markov Chain Monte Carlo (MCMC) algorithm that constructs a probability distribution for θ using Bayes theorem [55] [56] [57] [58] . MCMC requires the likelihood function to be known. This can be obviated, though, by assuming the distrib.....
    Document: The two dominant approaches to fitting models to data are least-squares estimation (LSE), which has been largely supplanted by the maximum likelihood estimation (MLE) [54] . The latter is typically embedded in a Markov Chain Monte Carlo (MCMC) algorithm that constructs a probability distribution for θ using Bayes theorem [55] [56] [57] [58] . MCMC requires the likelihood function to be known. This can be obviated, though, by assuming the distribution of model outcomes to be Poisson (as we do below), using likelihood-function-free methods [59] , or using approximate Bayesian approaches [60] .

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