View source: R/debiasing_algorithm.R
Sample two MCMC chains, each following single_kernel
marginally,
and coupled_kernel
jointly, until min(max(tau, m), max_iterations), where tau
is the first time at which the two chains meet (i.e. take the same value exactly).
Or more precisely, they meet with a delay of one, i.e. X_t = Y_t-1. The chains
are initialized from the distribution provided in rinit
.
See get_hmc_kernel
for an example of function returning the appropriate kernels.
1 2 | unbiased_estimator(single_kernel, coupled_kernel, rinit, h = function(x)
x, k = 0, m = 1, max_iterations = Inf)
|
single_kernel |
function taking a state (in a vector), its log density and an iteration, and returning
a list with a key named |
coupled_kernel |
function taking two states (in two vectors), their log densities and an iteration,
and returning a list with keys |
rinit |
function taking no arguments are returning an initial state for a Markov chain. |
h |
test function (possibly vector-valued) |
k |
burn-in parameter (default to 0) |
m |
time average parameter (will be proportional to the computing cost if meeting occurs before |
max_iterations |
number of iterations at which the function stops if it is still running (default to Inf). |
logtarget |
function evaluating the log target density |
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