  We present our submission to the SIGTYP 2020 Shared Task on the prediction of
typological features. We submit a constrained system, predicting typological
features only based on the WALS database. We investigate two approaches. The
simpler of the two is a system based on estimating correlation of feature
values within languages by computing conditional probabilities and mutual
information. The second approach is to train a neural predictor operating on
precomputed language embeddings based on WALS features. Our submitted system
combines the two approaches based on their self-estimated confidence scores. We
reach the accuracy of 70.7% on the test data and rank first in the shared task.
