Putting Iterative Proportional Fitting on the Researcher’s Desk

On the Iterative Proportional Fitting Procedure: Structure of Accumulation Points and L1-error Analysis

Design of Iterative Proportional Fitting Procedure for Possibility Distributions

We design an iterative proportional fitting procedure (parameterized by a continuous t-norm) for computation of multidimensional possibility distributions from its marginals, and discuss its basic properties.

Probability Measures with given Marginals and Conditionals: I-projections and Conditional Iterative Proportional Fitting

The iterative proportional fitting procedure (IPF-P) is an algorithm to compute approximately probability measures with prescribed marginals. We propose two extensions of the IPF-P, called conditional iterative proportional fitting procedures (CIPF-P), so that, additionally, given conditional distributions are taken into account. This modification is carried out by using the geometrical interpr...

Symmetric Iterative Proportional Fitting

Iterative Proportional Fitting (IPF) generates from an input matrix W a sequence of matrices that converges, under certain conditions, to a specific limit matrix Ŵ . This limit is the relative-entropy nearest solution to W among all matrices of prescribed row marginals r and column marginals c. We prove this known fact by a novel strategy that contributes a pure algorithmic intuition. Then we f...

Symmetric Iterative Proportional Fitting

This supplement consists of several parts that refer directly to specific topics in the paper: A Proof of Equation (2) B Proof of Lemma 3.1 (Symmetric biproportional fit) C Technical details on why ”local affinity” is sufficient in Section 4.1 D Proof of Theorem 4.2 (Convergence of PSIPF) E Proof of Lemma 4.4 (L1-monotony) F Proof of Lemma 4.5 (Volume bounds) G Proof of Lemma 4.6 (Limit points)...