WeibullTransitionPriors: Build a path specific Weibull TransitionPriors object, with...

In grantbrown/ABSEIR: Posterior Approximation to Fully Bayesian Spatial SEIR Models Using Multithreaded C++

Description

Build a path specific Weibull TransitionPriors object, with independent gamma priors for the shape/scale parameters of the latent and infectious time distributions.

Usage

WeibullTransitionPriors(
  latent_shape_prior_alpha,
  latent_shape_prior_beta,
  latent_scale_prior_alpha,
  latent_scale_prior_beta,
  infectious_shape_prior_alpha,
  infectious_shape_prior_beta,
  infectious_scale_prior_alpha,
  infectious_scale_prior_beta
)

Arguments

latent_shape_prior_alpha	The alpha (shape) value for the gamma hyperprior on the latent period shape parameter
latent_shape_prior_beta	The beta (rate) value for the gamma hyperprior on the latent period shape parameter
latent_scale_prior_alpha	The alpha (shape) value for the gamma hyperprior on the latent period scale parameter
latent_scale_prior_beta	The beta (rate) value for the gamma hyperprior on the latent period scale parameter
infectious_shape_prior_alpha	The alpha (shape) value for the gamma hyperprior on the infectious period shape parameter
infectious_shape_prior_beta	The beta (rate) value for the gamma hyperprior on the infectious period shape parameter
infectious_scale_prior_alpha	The alpha (shape) value for the gamma hyperprior on the infectious period scale parameter
infectious_scale_prior_beta	The beta (rate) value for the gamma hyperprior on the infectious period scale parameter

Details

WeibullTransitionPriors assumes that both the latent and infectious times are distributed according to Weibull distributions. Let τ_{lat} and τ_{inf} represent random variables corresponding to latent and infectious times. We then have: τ_{lat} \sim Weibull(θ_1^{lat}, θ_2^{lat}) τ_{inf} \sim Weibull(θ_1^{inf}, theta_2^{inf}) For each of these four parameters, we assign a gamma prior, parameterized by shape and rate: f(θ_{.}^{.}| α, β^{-1}) = frac{1}{Γ(α) β^α}e^{-\frac{x}{β}}x^{α-1}

These hyperparameters are specified by the eight arguments to WeibullTransitionPriors.

See Also

TransitionPriors, PathSpecificTransitionPriors, ExponentialTransitionPriors

Examples

transitionPriors <- WeibullTransitionPriors(35,20,150,20,90,20,220,20)

grantbrown/ABSEIR documentation built on Oct. 14, 2021, 2:32 p.m.