21,778 research outputs found
Learning loopy graphical models with latent variables: Efficient methods and guarantees
The problem of structure estimation in graphical models with latent variables
is considered. We characterize conditions for tractable graph estimation and
develop efficient methods with provable guarantees. We consider models where
the underlying Markov graph is locally tree-like, and the model is in the
regime of correlation decay. For the special case of the Ising model, the
number of samples required for structural consistency of our method scales
as , where p is the
number of variables, is the minimum edge potential, is
the depth (i.e., distance from a hidden node to the nearest observed nodes),
and is a parameter which depends on the bounds on node and edge
potentials in the Ising model. Necessary conditions for structural consistency
under any algorithm are derived and our method nearly matches the lower bound
on sample requirements. Further, the proposed method is practical to implement
and provides flexibility to control the number of latent variables and the
cycle lengths in the output graph.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1070 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Developing and applying heterogeneous phylogenetic models with XRate
Modeling sequence evolution on phylogenetic trees is a useful technique in
computational biology. Especially powerful are models which take account of the
heterogeneous nature of sequence evolution according to the "grammar" of the
encoded gene features. However, beyond a modest level of model complexity,
manual coding of models becomes prohibitively labor-intensive. We demonstrate,
via a set of case studies, the new built-in model-prototyping capabilities of
XRate (macros and Scheme extensions). These features allow rapid implementation
of phylogenetic models which would have previously been far more
labor-intensive. XRate's new capabilities for lineage-specific models,
ancestral sequence reconstruction, and improved annotation output are also
discussed. XRate's flexible model-specification capabilities and computational
efficiency make it well-suited to developing and prototyping phylogenetic
grammar models. XRate is available as part of the DART software package:
http://biowiki.org/DART .Comment: 34 pages, 3 figures, glossary of XRate model terminolog
Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models
This report addresses state inference for hidden Markov models. These models
rely on unobserved states, which often have a meaningful interpretation. This
makes it necessary to develop diagnostic tools for quantification of state
uncertainty. The entropy of the state sequence that explains an observed
sequence for a given hidden Markov chain model can be considered as the
canonical measure of state sequence uncertainty. This canonical measure of
state sequence uncertainty is not reflected by the classic multivariate state
profiles computed by the smoothing algorithm, which summarizes the possible
state sequences. Here, we introduce a new type of profiles which have the
following properties: (i) these profiles of conditional entropies are a
decomposition of the canonical measure of state sequence uncertainty along the
sequence and makes it possible to localize this uncertainty, (ii) these
profiles are univariate and thus remain easily interpretable on tree
structures. We show how to extend the smoothing algorithms for hidden Markov
chain and tree models to compute these entropy profiles efficiently.Comment: Submitted to Journal of Machine Learning Research; No RR-7896 (2012
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
Comparing Probabilistic Models for Melodic Sequences
Modelling the real world complexity of music is a challenge for machine
learning. We address the task of modeling melodic sequences from the same music
genre. We perform a comparative analysis of two probabilistic models; a
Dirichlet Variable Length Markov Model (Dirichlet-VMM) and a Time Convolutional
Restricted Boltzmann Machine (TC-RBM). We show that the TC-RBM learns
descriptive music features, such as underlying chords and typical melody
transitions and dynamics. We assess the models for future prediction and
compare their performance to a VMM, which is the current state of the art in
melody generation. We show that both models perform significantly better than
the VMM, with the Dirichlet-VMM marginally outperforming the TC-RBM. Finally,
we evaluate the short order statistics of the models, using the
Kullback-Leibler divergence between test sequences and model samples, and show
that our proposed methods match the statistics of the music genre significantly
better than the VMM.Comment: in Proceedings of the ECML-PKDD 2011. Lecture Notes in Computer
Science, vol. 6913, pp. 289-304. Springer (2011
Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
We present a new method for nonlinear prediction of discrete random sequences
under minimal structural assumptions. We give a mathematical construction for
optimal predictors of such processes, in the form of hidden Markov models. We
then describe an algorithm, CSSR (Causal-State Splitting Reconstruction), which
approximates the ideal predictor from data. We discuss the reliability of CSSR,
its data requirements, and its performance in simulations. Finally, we compare
our approach to existing methods using variable-length Markov models and
cross-validated hidden Markov models, and show theoretically and experimentally
that our method delivers results superior to the former and at least comparable
to the latter.Comment: 8 pages, 4 figure
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