inf_time_proposal: PhD A Gibbs Sampler for a partially observed SIR epidemic.
In JMacDonaldPhD/Epidemics: Inference For Epidemics (one line, title case)
View source: R/Centered_MCMC.R
Description
A Centered Parameterisation of Bayesian Modelling of the Infection and Removal times
of an Epidemic Centered
Usage
1
inf_time_proposal(t_inf, t_rem, no_proposals, par_rem)
Details
==== Model ====
theta(beta, gamma) –> Infection Times (I, Unobserved) –> Removal Times (R, Observed)
Approach is based on that used by Neal & Roberts (2003).
1. Take observed data and simulate a set of valid infection times using inital values
for gamma and beta.
Steps for one iteration:
2. Calculate the parameters of the conditional posteriors
to be sampled from.
3. Sample from the conditional distributions for the infection and removal rate
in turn
4. Sample an Infection Time to change. Propose the change and Accept/Reject using
Metropolis-Hastings Acceptance probability
JMacDonaldPhD/Epidemics documentation built on Jan. 10, 2020, 2:48 a.m.
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View source: R/Centered_MCMC.R
Description
A Centered Parameterisation of Bayesian Modelling of the Infection and Removal times of an Epidemic Centered
Usage
1 | inf_time_proposal(t_inf, t_rem, no_proposals, par_rem)
|
Details
==== Model ====
theta(beta, gamma) –> Infection Times (I, Unobserved) –> Removal Times (R, Observed)
Approach is based on that used by Neal & Roberts (2003).
1. Take observed data and simulate a set of valid infection times using inital values for gamma and beta.
Steps for one iteration:
2. Calculate the parameters of the conditional posteriors to be sampled from.
3. Sample from the conditional distributions for the infection and removal rate in turn
4. Sample an Infection Time to change. Propose the change and Accept/Reject using Metropolis-Hastings Acceptance probability
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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