175 research outputs found
Latent tree models
Latent tree models are graphical models defined on trees, in which only a
subset of variables is observed. They were first discussed by Judea Pearl as
tree-decomposable distributions to generalise star-decomposable distributions
such as the latent class model. Latent tree models, or their submodels, are
widely used in: phylogenetic analysis, network tomography, computer vision,
causal modeling, and data clustering. They also contain other well-known
classes of models like hidden Markov models, Brownian motion tree model, the
Ising model on a tree, and many popular models used in phylogenetics. This
article offers a concise introduction to the theory of latent tree models. We
emphasise the role of tree metrics in the structural description of this model
class, in designing learning algorithms, and in understanding fundamental
limits of what and when can be learned
Automorphism groups of Gaussian chain graph models
In this paper we extend earlier work on groups acting on Gaussian graphical
models to Gaussian Bayesian networks and more general Gaussian models defined
by chain graphs. We discuss the maximal group which leaves a given model
invariant and provide basic statistical applications of this result. This
includes equivariant estimation, maximal invariants and robustness. The
computation of the group requires finding the essential graph. However, by
applying Studeny's theory of imsets we show that computations for DAGs can be
performed efficiently without building the essential graph. In our proof we
derive simple necessary and sufficient conditions on vanishing sub-minors of
the concentration matrix in the model
The Dependence of Routine Bayesian Model Selection Methods on Irrelevant Alternatives
Bayesian methods - either based on Bayes Factors or BIC - are now widely used
for model selection. One property that might reasonably be demanded of any
model selection method is that if a model is preferred to a model
, when these two models are expressed as members of one model class
, this preference is preserved when they are embedded in a
different class . However, we illustrate in this paper that with
the usual implementation of these common Bayesian procedures this property does
not hold true even approximately. We therefore contend that to use these
methods it is first necessary for there to exist a "natural" embedding class.
We argue that in any context like the one illustrated in our running example of
Bayesian model selection of binary phylogenetic trees there is no such
embedding
Tree cumulants and the geometry of binary tree models
In this paper we investigate undirected discrete graphical tree models when
all the variables in the system are binary, where leaves represent the
observable variables and where all the inner nodes are unobserved. A novel
approach based on the theory of partially ordered sets allows us to obtain a
convenient parametrization of this model class. The construction of the
proposed coordinate system mirrors the combinatorial definition of cumulants. A
simple product-like form of the resulting parametrization gives insight into
identifiability issues associated with this model class. In particular, we
provide necessary and sufficient conditions for such a model to be identified
up to the switching of labels of the inner nodes. When these conditions hold,
we give explicit formulas for the parameters of the model. Whenever the model
fails to be identified, we use the new parametrization to describe the geometry
of the unidentified parameter space. We illustrate these results using a simple
example.Comment: Published in at http://dx.doi.org/10.3150/10-BEJ338 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Total positivity in exponential families with application to binary variables
We study exponential families of distributions that are multivariate totally
positive of order 2 (MTP2), show that these are convex exponential families,
and derive conditions for existence of the MLE. Quadratic exponential familes
of MTP2 distributions contain attractive Gaussian graphical models and
ferromagnetic Ising models as special examples. We show that these are defined
by intersecting the space of canonical parameters with a polyhedral cone whose
faces correspond to conditional independence relations. Hence MTP2 serves as an
implicit regularizer for quadratic exponential families and leads to sparsity
in the estimated graphical model. We prove that the maximum likelihood
estimator (MLE) in an MTP2 binary exponential family exists if and only if both
of the sign patterns and are represented in the sample for
every pair of variables; in particular, this implies that the MLE may exist
with observations, in stark contrast to unrestricted binary exponential
families where observations are required. Finally, we provide a novel and
globally convergent algorithm for computing the MLE for MTP2 Ising models
similar to iterative proportional scaling and apply it to the analysis of data
from two psychological disorders
Secant cumulants and toric geometry
We study the secant line variety of the Segre product of projective spaces
using special cumulant coordinates adapted for secant varieties. We show that
the secant variety is covered by open normal toric varieties. We prove that in
cumulant coordinates its ideal is generated by binomial quadrics. We present
new results on the local structure of the secant variety. In particular, we
show that it has rational singularities and we give a description of the
singular locus. We also classify all secant varieties that are Gorenstein.
Moreover, generalizing (Sturmfels and Zwiernik 2012), we obtain analogous
results for the tangential variety.Comment: Some improvements to previous results, with other minor changes.
Updated reference
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