Selected article for: "discount factor and gaussian distribution"
Author: Pei, Sen; Morone, Flaviano; Liljeros, Fredrik; Makse, Hernán; Shaman, Jeffrey L
Title: Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus
  • Document date: 2018_12_18
    • ID: 0dut9fjn_43
    Snippet: Input: An agent-based model M in a time-varying contact network GðV; E; tÞ, the number of observations T, incidence fo t g, the observational error variance (OEV) fs 2 t;o g, the initial system parameters z 0 ¼ ðb; I 0 ; C 0 Þ T , the initial covariance matrix S, a discount factor a 2 ð0; 1Þ, and the number of iterations L. for l ¼ 1 to L do Generate an ensemble of parameter vectors with n members using a multivariate Gaussian distributio.....
    Document: Input: An agent-based model M in a time-varying contact network GðV; E; tÞ, the number of observations T, incidence fo t g, the observational error variance (OEV) fs 2 t;o g, the initial system parameters z 0 ¼ ðb; I 0 ; C 0 Þ T , the initial covariance matrix S, a discount factor a 2 ð0; 1Þ, and the number of iterations L. for l ¼ 1 to L do Generate an ensemble of parameter vectors with n members using a multivariate Gaussian distribution: fẑ l 0 g n~N ð z lÀ1 ; a 2ðlÀ1Þ SÞ. for t ¼ 1 to T do Run the agent-based model M with posterior fẑ l tÀ1 g n obtained from last update for one week, and return the ensemble of incidence: fo l t g n ¼ MðG; fẑ l tÀ1 g n Þ. Update the prior distribution of parameters b, I 0 and C 0 : z l t È É n fẑ l tÀ1 g n to posterior fẑ l t g n using the EAKF, fo l t g n , fs 2 t;o g and fo t g. Individual states are evolved per the agent-based model and are not updated by the EAKF. end for Calculate the ensemble mean of the posterior over time as the input in next iteration:

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