  Consider the problem of learning undirected graphical models on trees from
corrupted data. Recently Katiyar et al. showed that it is possible to recover
trees from noisy binary data up to a small equivalence class of possible trees.
Their other paper on the Gaussian case follows a similar pattern. By framing
this as a special phylogenetic recovery problem we largely generalize these two
settings. Using the framework of linear latent tree models we discuss tree
identifiability for binary data under a continuous corruption model. For the
Ising and the Gaussian tree model we also provide a characterisation of when
the Chow-Liu algorithm consistently learns the underlying tree from the noisy
data.
