### Non-negative sparse priors

Sparseness priors, which impose that most of the weights are small or zero, are very effective in constraining regression problems. The prototypical sparseness prior is the Laplacian prior (aka L1-prior), which imposes a penalty on the absolute value of individual weights. Regression problems (and GLMs) with Laplacian priors can be easily solved by Maximum a … More Non-negative sparse priors