Create a simple random walk sampler, which uses a symmetric proposal to move around parameter space. This sampler supports sampling from models where the likelihood is only computable randomly (e.g., for pmcmc).
Usage
monty_sampler_random_walk(
vcv = NULL,
boundaries = "reflect",
rerun_every = Inf,
rerun_random = TRUE
)Arguments
- vcv
A variance covariance matrix for the proposal.
- boundaries
Control the behaviour of proposals that are outside the model domain. The supported options are:
"reflect" (the default): we reflect proposed parameters that lie outside the domain back into the domain (as many times as needed)
"reject": we do not evaluate the density function, and return
-Inffor its density instead."ignore": evaluate the point anyway, even if it lies outside the domain.
The initial point selected will lie within the domain, as this is enforced by monty_sample.
- rerun_every
Optional integer giving the frequency at which we should rerun the model on the current "accepted" parameters to obtain a new density for stochastic models. The default for this (
Inf) will never trigger a rerun, but if you set to 100, then every 100 steps we run the model on both the proposed and previously accepted parameters before doing the comparison. This may help "unstick" chains, at the cost of some bias in the results.- rerun_random
Logical, controlling the behaviour of rerunning (when
rerun_everyis finite). With the default value ofTRUE, we stochastically rerun at each step with probability of1 / rerun_every. IfFALSEwe rerun the model at fixed intervals of iterations (given byrerun_every). The two methods give the same expected number of MCMC steps between reruns but a different pattern.
Value
A monty_sampler object, which can be used with
monty_sample