# Sampling from TMB models using Stan

#### Kasper Kristensen

#### 2026-07-23

# How to sample

We consider the random regression example from the `RTMB` introduction vignette:

    ## Data
    data(ChickWeight)

    ## Parameters and random effects
    parameters <- list(
        mua=0,          ## Mean slope
        sda=1,          ## Std of slopes
        mub=0,          ## Mean intercept
        sdb=1,          ## Std of intercepts
        sdeps=1,        ## Residual Std
        a=rep(0, 50),   ## Random slope by chick
        b=rep(0, 50)    ## Random intercept by chick
    )

    ## Negative log likelihood
    f <- function(parms) {
        getAll(ChickWeight, parms, warn=FALSE)
        ## Optional (enables extra RTMB features)
        weight <- OBS(weight)
        ## Initialize joint negative log likelihood
        nll <- 0
        ## Random slopes
        nll <- nll - sum(dnorm(a, mean=mua, sd=sda, log=TRUE))
        ## Random intercepts
        nll <- nll - sum(dnorm(b, mean=mub, sd=sdb, log=TRUE))
        ## Data
        predWeight <- a[Chick] * Time + b[Chick]
        nll <- nll - sum(dnorm(weight, predWeight, sd=sdeps, log=TRUE))
        ## Get predicted weight uncertainties
        ADREPORT(predWeight)
        ## Return
        nll
    }

We build the `TMB` model object (following the original example, we specify the `random` effects although that is redundant here):

    obj <- MakeADFun(f, parameters, random=c("a", "b"))

A full Bayesian analysis is run by:

    out <- tmbstan(obj, chains=1, seed=1)

    ##
    ## SAMPLING FOR MODEL 'tmb_generic' NOW (CHAIN 1).
    ## Chain 1:
    ## Chain 1: Gradient evaluation took 4.7e-05 seconds
    ## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.
    ## Chain 1: Adjust your expectations accordingly!
    ## Chain 1:
    ## Chain 1:
    ## Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
    ## Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
    ## Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
    ## Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
    ## Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
    ## Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
    ## Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
    ## Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
    ## Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
    ## Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
    ## Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
    ## Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
    ## Chain 1:
    ## Chain 1:  Elapsed Time: 1.71 seconds (Warm-up)
    ## Chain 1:                0.902 seconds (Sampling)
    ## Chain 1:                2.612 seconds (Total)
    ## Chain 1:

# Improving sampling speed

MCMC runs for large models can take a very long time. A simple strategy for improving sampling speed is to speed up the objective function and gradient by enabling vectorization.

    TapeConfig(vectorize="enable")  ## Enable vectorization
    obj_vect <- MakeADFun(f, parameters, random=c("a", "b"))
    TapeConfig(vectorize="disable") ## Restore previous settings

We can check the effect of this optimization before starting the potentially long MCMC run. For accurate timing, excluding R overhead, we use the `RTMB` tape `timer()` method, and report an average over `1e4` evaluations of the `total` tape pass:

    ## Timing without vectorization
    t <- GetTape(obj)$timer(rep=1e4,total=TRUE)
    ## Timing with vectorization
    t_vect <- GetTape(obj_vect)$timer(rep=1e4,total=TRUE)
    ## speedup ?
    c(speedup = unname(t / t_vect))

    ##  speedup
    ## 3.177701

In this case vectorization appears to be beneficial. Let’s compare the MCMC runs:

    system.time(out <- tmbstan(obj, chains=1, seed=1, refresh = 0))

    ##    user  system elapsed
    ##   2.638   0.000   2.639

    system.time(out_vect <- tmbstan(obj_vect, chains=1, seed=1, refresh = 0))

    ##    user  system elapsed
    ##   0.979   0.001   0.981