# 7 Variational Inference using ADVI

The CmdStan method variational uses the Automatic Differentiation Variational Inference (ADVI) algorithm of Kucukelbir et al. (2017) to provide an approximate posterior distribution of the model conditioned on the data. The approximating distribution it uses is a Gaussian in the unconstrained variable space, either a fully factorized Gaussian approximation, specified by argument algorithm=meanfield option, or a Gaussian approximation using a full-rank covariance matrix, specified by argument algorithm=fullrank. By default, ADVI uses option algorithm=meanfield.

The following is a minimal call to Stan’s variational inference algorithm using defaults for everything but the location of the data file.

> ./bernoulli variational data file=bernoulli.data.R

Executing this command prints both output to the console and to a csv file.

The first part of the console output reports on the configuration used: the default option algorithm=meanfield and the default tolerances for monitoring the algorithm’s convergence.

method = variational
variational
algorithm = meanfield (Default)
meanfield
iter = 10000 (Default)
elbo_samples = 100 (Default)
eta = 1 (Default)
engaged = 1 (Default)
iter = 50 (Default)
tol_rel_obj = 0.01 (Default)
eval_elbo = 100 (Default)
output_samples = 1000 (Default)
id = 0 (Default)
data
file = bernoulli.data.json
init = 2 (Default)
random
seed = 3323783840 (Default)
output
file = output.csv (Default)
diagnostic_file =  (Default)
refresh = 100 (Default)

After the configuration has been displayed, informational and timing messages are output:

------------------------------------------------------------
EXPERIMENTAL ALGORITHM:
This procedure has not been thoroughly tested and may be unstable
or buggy. The interface is subject to change.
------------------------------------------------------------

1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
Adjust your expectations accordingly!

The rest of the output describes the progression of the algorithm. An adaptation phase finds a good value for the step size scaling parameter eta. The evidence lower bound (ELBO) is the variational objective function and is evaluated based on a Monte Carlo estimate. The variational inference algorithm in Stan is stochastic, which makes it challenging to assess convergence. That is, while the algorithm appears to have converged in $$\sim$$ 250 iterations, the algorithm runs for another few thousand iterations until mean change in ELBO drops below the default tolerance of 0.01.

Begin eta adaptation.
Iteration:   1 / 250 [  0%]  (Adaptation)
Iteration:  50 / 250 [ 20%]  (Adaptation)
Iteration: 100 / 250 [ 40%]  (Adaptation)
Iteration: 150 / 250 [ 60%]  (Adaptation)
Iteration: 200 / 250 [ 80%]  (Adaptation)
Success! Found best value [eta = 1] earlier than expected.

iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes
100           -6.131             1.000            1.000
200           -6.458             0.525            1.000
300           -6.300             0.359            0.051
400           -6.137             0.276            0.051
500           -6.243             0.224            0.027
600           -6.305             0.188            0.027
700           -6.289             0.162            0.025
800           -6.402             0.144            0.025
900           -6.103             0.133            0.025
1000           -6.314             0.123            0.027
1100           -6.348             0.024            0.025
1200           -6.244             0.020            0.018
1300           -6.293             0.019            0.017
1400           -6.250             0.017            0.017
1500           -6.241             0.015            0.010   MEDIAN ELBO CONVERGED

Drawing a sample of size 1000 from the approximate posterior...
COMPLETED.

The output from variational is written into the file output.csv by default. The output follows the same pattern as the output for sampling, first dumping the entire set of parameters used as CSV comments:

# stan_version_major = 2
# stan_version_minor = 23
# stan_version_patch = 0
# model = bernoulli_model
# method = variational
#   variational
#     algorithm = meanfield (Default)
#       meanfield
#     iter = 10000 (Default)
#     elbo_samples = 100 (Default)
#     eta = 1 (Default)
#       engaged = 1 (Default)
#       iter = 50 (Default)
#     tol_rel_obj = 0.01 (Default)
#     eval_elbo = 100 (Default)
#     output_samples = 1000 (Default)
...

Next is the column header line, followed more CSV comments reporting the adapted value for the stepsize, followed by the values. The first line is special: it is the mean of the variational approximation. The rest of the output contains output_samples number of samples drawn from the variational approximation.

lp__,log_p__,log_g__,theta
# eta = 1
0,0,0,0.236261
0,-6.82318,-0.0929121,0.300415
0,-6.89701,-0.158687,0.321982
0,-6.99391,-0.23916,0.343643
0,-7.35801,-0.51787,0.401554
0,-7.4668,-0.539473,0.123081
...

The header indicates the unnormalized log probability with lp__. This is a legacy feature that we do not use for variational inference. The ELBO is not stored unless a diagnostic option is given.

### Bibliography

Kucukelbir, Alp, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M Blei. 2017. “Automatic Differentiation Variational Inference.” Journal of Machine Learning Research.