LINKING WITH CONTINUOUS EXPONENTIAL FAMILIES: SINGLE-GROUP DESIGNS

SMML estimators for exponential families with continuous sufficient statistics

The minimum message length principle is an information theoretic criterion that links data compression with statistical inference. This paper studies the strict minimum message length (SMML) estimator for d-dimensional exponential families with continuous sufficient statistics, for all d. The partition of an SMML estimator is shown to consist of convex polytopes (i.e. convex polygons when d = 2...

Satisfiability with Exponential Families

Fix a set S ⊆ {0, 1}∗ of exponential size, e.g. |S ∩ {0, 1}n| ∈ Ω(αn), α > 1. The S-SAT problem asks whether a propositional formula F over variables v1, . . . , vn has a satisfying assignment (v1, . . . , vn) ∈ {0, 1}n ∩S. Our interest is in determining the complexity of S-SAT. We prove that S-SAT is NP-complete for all context-free sets S. Furthermore, we show that if S-SAT is in P for some e...

Exponential families

Estimating Densities with Non-Parametric Exponential Families

We propose a novel approach for density estimation with exponential families for the case when the true density may not fall within the chosen family. Our approach augments the sufficient statistics with features designed to accumulate probability mass in the neighborhood of the observed points, resulting in a non-parametric model similar to kernel density estimators. We show that under mild co...

FastEx: Hash Clustering with Exponential Families

Clustering is a key component in any data analysis toolbox. Despite its importance, scalable algorithms often eschew rich statistical models in favor of simpler descriptions such as k-means clustering. In this paper we present a sampler, capable of estimating mixtures of exponential families. At its heart lies a novel proposal distribution using random projections to achieve high throughput in ...