# Time Comparisons

All samplers run for 1024 iterations.

## C++

    X <- bench::mark(
        "Metropolis-Hastings" = {samplr::sampler_mh(1, "norm", c(0,1), sigma_prop=1)},
        "MC3" = {samplr::sampler_mc3(1, "norm", c(0,1), sigma_prop=1)},
        "Hamiltonian Monte-Carlo" = {samplr::sampler_hmc(1, "norm", c(0,1))},
        "REC" = {samplr::sampler_rec(1, "norm", c(0,1))},
        "MCHMC" = {samplr::sampler_mchmc(1, "norm", c(0,1), )},
        "MCREC" = {samplr::sampler_mcrec(1, "norm", c(0,1))},
        check = FALSE, iterations = 50
    )
    #> Warning: Some expressions had a GC in every iteration; so filtering is
    #> disabled.
    knitr::kable(as.data.frame(X[,c("expression", "min", "median")]))

expression | min | median
--- | --- | ---
Metropolis-Hastings | 944.3µs | 1.32ms
MC3 | 13.53ms | 15.96ms
Hamiltonian Monte-Carlo | 9.98ms | 12.19ms
REC | 10.42ms | 12.98ms
MCHMC | 79.84ms | 86.51ms
MCREC | 76.67ms | 87.77ms

## MATLAB

tests | timeit
--- | ---
Metropolis-Hastings | 6.22ms
MC3 | 55.13ms