Random Function Priors for Correlation Modeling

Code

• github.com/rmehta1987/CoZINB
  pytorch★ 0

Abstract

The likelihood model of high dimensional data X_n can often be expressed as p(X_n|Z_n,), where :=(_k)_k[K] is a collection of hidden features shared across objects, indexed by n, and Z_n is a non-negative factor loading vector with K entries where Z_nk indicates the strength of _k used to express X_n. In this paper, we introduce random function priors for Z_n for modeling correlations among its K dimensions Z_n1 through Z_nK, which we call population random measure embedding (PRME). Our model can be viewed as a generalized paintbox model~Broderick13 using random functions, and can be learned efficiently with neural networks via amortized variational inference. We derive our Bayesian nonparametric method by applying a representation theorem on separately exchangeable discrete random measures.

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Reproductions