56 research outputs found
An Implicitization Challenge for Binary Factor Analysis
We use tropical geometry to compute the multidegree and Newton polytope of
the hypersurface of a statistical model with two hidden and four observed
binary random variables, solving an open question stated by Drton, Sturmfels
and Sullivant in "Lectures on Algebraic Statistics" (Problem 7.7). The model is
obtained from the undirected graphical model of the complete bipartite graph
by marginalizing two of the six binary random variables. We present
algorithms for computing the Newton polytope of its defining equation by
parallel walks along the polytope and its normal fan. In this way we compute
vertices of the polytope. Finally, we also compute and certify its facets by
studying tangent cones of the polytope at the symmetry classes vertices. The
Newton polytope has 17214912 vertices in 44938 symmetry classes and 70646
facets in 246 symmetry classes.Comment: 25 pages, 5 figures, presented at Mega 09 (Barcelona, Spain
Implicitization of curves and (hyper)surfaces using predicted support
We reduce implicitization of rational planar parametric curves and (hyper)surfaces to linear algebra, by interpolating the coefficients of the implicit equation.
For predicting the implicit support, we focus on methods that exploit input and output structure in the sense of sparse (or toric) elimination theory, namely by computing the Newton polytope of the implicit polynomial, via sparse resultant theory.
Our algorithm works even in the presence of base points but, in this case, the implicit equation shall be obtained as a factor of the produced polynomial.
We implement our methods on Maple, and some on Matlab as well, and study their numerical stability and efficiency on several classes of curves and surfaces.
We apply our approach to approximate implicitization,
and quantify the accuracy of the approximate output,
which turns out to be satisfactory on all tested examples; we also relate our measures to Hausdorff distance.
In building a square or rectangular matrix, an important issue is (over)sampling the given curve or surface: we conclude that unitary complexes offer the best tradeoff between speed and accuracy when numerical methods are employed, namely SVD, whereas for exact kernel computation random integers is the method of choice.
We compare our prototype to existing software and find that it is rather competitive
Implicitization of surfaces via geometric tropicalization
In this paper we further develop the theory of geometric tropicalization due
to Hacking, Keel and Tevelev and we describe tropical methods for
implicitization of surfaces. More precisely, we enrich this theory with a
combinatorial formula for tropical multiplicities of regular points in
arbitrary dimension and we prove a conjecture of Sturmfels and Tevelev
regarding sufficient combinatorial conditions to compute tropical varieties via
geometric tropicalization. Using these two results, we extend previous work of
Sturmfels, Tevelev and Yu for tropical implicitization of generic surfaces, and
we provide methods for approaching the non-generic cases.Comment: 20 pages, 6 figures. Mayor reorganization and exposition improved.
The results on geometric tropicalization have been extended to any dimension.
In particular, Conjecture 2.8 is now Theorem 2.
Model testing for causal models
Finding cause-effect relationships is the central aim of many studies in the physical, behavioral, social and biological sciences. We consider two well-known mathematical causal models: Structural equation models and causal Bayesian networks. When we hypothesize a causal model, that model often imposes constraints on the statistics of the data collected. These constraints enable us to test or falsify the hypothesized causal model. The goal of our research is to develop efficient and reliable methods to test a causal model or distinguish between causal models using various types of constraints.
For linear structural equation models, we investigate the problem of generating a small number of constraints in the form of zero partial correlations, providing an efficient way to test hypothesized models. We study linear structural equation models with correlated errors focusing on the graphical aspects of the models. We provide a set of local Markov properties and prove that they are equivalent to the global Markov property.
For causal Bayesian networks, we study equality and inequality constraints imposed on data and investigate a way to use these constraints for model testing and selection. For equality constraints, we formulate an implicitization problem and show how we may reduce the complexity of the problem. We also study the algebraic structure of the equality constraints. For inequality constraints, we present a class of inequality constraints on both nonexperimental and interventional distributions
Tropical secant graphs of monomial curves
The first secant variety of a projective monomial curve is a threefold with
an action by a one-dimensional torus. Its tropicalization is a
three-dimensional fan with a one-dimensional lineality space, so the tropical
threefold is represented by a balanced graph. Our main result is an explicit
construction of that graph. As a consequence, we obtain algorithms to
effectively compute the multidegree and Chow polytope of an arbitrary
projective monomial curve. This generalizes an earlier degree formula due to
Ranestad. The combinatorics underlying our construction is rather delicate, and
it is based on a refinement of the theory of geometric tropicalization due to
Hacking, Keel and Tevelev.Comment: 30 pages, 8 figures. Major revision of the exposition. In particular,
old Sections 4 and 5 are merged into a single section. Also, added Figure 3
and discussed Chow polytopes of rational normal curves in Section
When Does a Mixture of Products Contain a Product of Mixtures?
We derive relations between theoretical properties of restricted Boltzmann
machines (RBMs), popular machine learning models which form the building blocks
of deep learning models, and several natural notions from discrete mathematics
and convex geometry. We give implications and equivalences relating
RBM-representable probability distributions, perfectly reconstructible inputs,
Hamming modes, zonotopes and zonosets, point configurations in hyperplane
arrangements, linear threshold codes, and multi-covering numbers of hypercubes.
As a motivating application, we prove results on the relative representational
power of mixtures of product distributions and products of mixtures of pairs of
product distributions (RBMs) that formally justify widely held intuitions about
distributed representations. In particular, we show that a mixture of products
requiring an exponentially larger number of parameters is needed to represent
the probability distributions which can be obtained as products of mixtures.Comment: 32 pages, 6 figures, 2 table
Mixtures and products in two graphical models
We compare two statistical models of three binary random variables. One is a
mixture model and the other is a product of mixtures model called a restricted
Boltzmann machine. Although the two models we study look different from their
parametrizations, we show that they represent the same set of distributions on
the interior of the probability simplex, and are equal up to closure. We give a
semi-algebraic description of the model in terms of six binomial inequalities
and obtain closed form expressions for the maximum likelihood estimates. We
briefly discuss extensions to larger models.Comment: 18 pages, 7 figure
Gibbs Manifolds
Gibbs manifolds are images of affine spaces of symmetric matrices under the
exponential map. They arise in applications such as optimization, statistics
and quantum~physics, where they extend the ubiquitous role of toric geometry.
The Gibbs variety is the zero locus of all polynomials that vanish on the Gibbs
manifold. We compute these polynomials and show that the Gibbs variety is
low-dimensional. Our theory is applied to a wide range of scenarios, including
matrix pencils and quantum optimal transport.Comment: 22 page
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