  We consider learning a sparse pairwise Markov Random Field (MRF) with
continuous-valued variables from i.i.d samples. We adapt the algorithm of
Vuffray et al. (2019) to this setting and provide finite-sample analysis
revealing sample complexity scaling logarithmically with the number of
variables, as in the discrete and Gaussian settings. Our approach is applicable
to a large class of pairwise MRFs with continuous variables and also has
desirable asymptotic properties, including consistency and normality under mild
conditions. Further, we establish that the population version of the
optimization criterion employed in Vuffray et al. (2019) can be interpreted as
local maximum likelihood estimation (MLE). As part of our analysis, we
introduce a robust variation of sparse linear regression a` la Lasso, which may
be of interest in its own right.
