Author: Renato Machado Cotta; Carolina Palma Naveira-Cotta; pierre magal
Title: Modelling the COVID-19 epidemics in Brasil: Parametric identification and public health measures influence Document date: 2020_4_3
ID: 3rmrkzuq_33
Snippet: where ï° posterior (P) is the posterior probability density, that is, the conditional probability of the parameters P given the measurements Y, ï° prior (P) is the prior density, that is, the coded information about the parameters prior to the measurements, ï° (Y|P) is the likelihood function, which expresses the likelihood of different measurement outcomes involved, allowing one to do Bayesian inference even in rich and complex models. The id.....
Document: where ï° posterior (P) is the posterior probability density, that is, the conditional probability of the parameters P given the measurements Y, ï° prior (P) is the prior density, that is, the coded information about the parameters prior to the measurements, ï° (Y|P) is the likelihood function, which expresses the likelihood of different measurement outcomes involved, allowing one to do Bayesian inference even in rich and complex models. The idea behind the Metropolis-Hasting sampling algorithm is illustrated below, and these steps should be repeat until it is judged that a sufficiently representative sample has been generated.
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