1,199 research outputs found
Faithfulness and learning hypergraphs from discrete distributions
The concepts of faithfulness and strong-faithfulness are important for
statistical learning of graphical models. Graphs are not sufficient for
describing the association structure of a discrete distribution. Hypergraphs
representing hierarchical log-linear models are considered instead, and the
concept of parametric (strong-) faithfulness with respect to a hypergraph is
introduced. Strong-faithfulness ensures the existence of uniformly consistent
parameter estimators and enables building uniformly consistent procedures for a
hypergraph search. The strength of association in a discrete distribution can
be quantified with various measures, leading to different concepts of
strong-faithfulness. Lower and upper bounds for the proportions of
distributions that do not satisfy strong-faithfulness are computed for
different parameterizations and measures of association.Comment: 23 pages, 6 figure
Toric algebra of hypergraphs
The edges of any hypergraph parametrize a monomial algebra called the edge
subring of the hypergraph. We study presentation ideals of these edge subrings,
and describe their generators in terms of balanced walks on hypergraphs. Our
results generalize those for the defining ideals of edge subrings of graphs,
which are well-known in the commutative algebra community, and popular in the
algebraic statistics community. One of the motivations for studying toric
ideals of hypergraphs comes from algebraic statistics, where generators of the
toric ideal give a basis for random walks on fibers of the statistical model
specified by the hypergraph. Further, understanding the structure of the
generators gives insight into the model geometry.Comment: Section 3 is new: it explains connections to log-linear models in
algebraic statistics and to combinatorial discrepancy. Section 6 (open
problems) has been moderately revise
Kochen-Specker Sets and Generalized Orthoarguesian Equations
Every set (finite or infinite) of quantum vectors (states) satisfies
generalized orthoarguesian equations (OA). We consider two 3-dim
Kochen-Specker (KS) sets of vectors and show how each of them should be
represented by means of a Hasse diagram---a lattice, an algebra of subspaces of
a Hilbert space--that contains rays and planes determined by the vectors so as
to satisfy OA. That also shows why they cannot be represented by a special
kind of Hasse diagram called a Greechie diagram, as has been erroneously done
in the literature. One of the KS sets (Peres') is an example of a lattice in
which 6OA pass and 7OA fails, and that closes an open question of whether the
7oa class of lattices properly contains the 6oa class. This result is important
because it provides additional evidence that our previously given proof of noa
=< (n+1)oa can be extended to proper inclusion noa < (n+1)oa and that nOA form
an infinite sequence of successively stronger equations.Comment: 16 pages and 5 figure
Describing the complexity of systems: multi-variable "set complexity" and the information basis of systems biology
Context dependence is central to the description of complexity. Keying on the
pairwise definition of "set complexity" we use an information theory approach
to formulate general measures of systems complexity. We examine the properties
of multi-variable dependency starting with the concept of interaction
information. We then present a new measure for unbiased detection of
multi-variable dependency, "differential interaction information." This
quantity for two variables reduces to the pairwise "set complexity" previously
proposed as a context-dependent measure of information in biological systems.
We generalize it here to an arbitrary number of variables. Critical limiting
properties of the "differential interaction information" are key to the
generalization. This measure extends previous ideas about biological
information and provides a more sophisticated basis for study of complexity.
The properties of "differential interaction information" also suggest new
approaches to data analysis. Given a data set of system measurements
differential interaction information can provide a measure of collective
dependence, which can be represented in hypergraphs describing complex system
interaction patterns. We investigate this kind of analysis using simulated data
sets. The conjoining of a generalized set complexity measure, multi-variable
dependency analysis, and hypergraphs is our central result. While our focus is
on complex biological systems, our results are applicable to any complex
system.Comment: 44 pages, 12 figures; made revisions after peer revie
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