Description Usage Arguments Author(s)
View source: R/MetropHastings.R
Simple protocol for a MCMC sampling algorithm. Given a set of priors, and a likelihood function, the algorithm samples new values for N parameters from a standard normal with a jump variance that is automatically adjusted. The algoritm accepts new proposal proportional to a random fraction.
1 |
dat |
dataset to be based to the likelihood function |
start |
a vector of starting values |
LL |
a likelihood function that accepts the parameters and data |
priors |
functions that give the prior probability for the parameter |
N |
total number of sampling rounds (one rounds goes through all parameters once). |
giveup |
after how many rounds of no new accepted values does the algorithm give up and assume convergence? |
Marco D. Visser
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