Selected article for: "main analysis and sensitivity analysis"
Author: de Silva, Eric; Ferguson, Neil M.; Fraser, Christophe
Title: Inferring pandemic growth rates from sequence data
  • Document date: 2012_8_7
    • ID: 1piyoafd_54
    Snippet: As with any Bayesian analysis where there are parameters having little or no prior information, there exists the possibility that the posterior estimate is being unintentionally biased by the chosen priors. However, we have explored the use of different priors (as well as other MCMC proposals within BEAST) and find that all the main results reported here are robust to such sensitivity analysis. We also note that the recently developed Bayesian 's.....
    Document: As with any Bayesian analysis where there are parameters having little or no prior information, there exists the possibility that the posterior estimate is being unintentionally biased by the chosen priors. However, we have explored the use of different priors (as well as other MCMC proposals within BEAST) and find that all the main results reported here are robust to such sensitivity analysis. We also note that the recently developed Bayesian 'skyride' [29] method places less emphasis on priors using Gaussian Markov random fields to smooth the effective population size over time.

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