16,588 research outputs found
Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models
This report considers the problem of computing the Cramer-Rao bound for the
parameters of a Markov random field. Computation of the exact bound is not
feasible for most fields of interest because their likelihoods are intractable
and have intractable derivatives. We show here how it is possible to formulate
the computation of the bound as a statistical inference problem that can be
solve approximately, but with arbitrarily high accuracy, by using a Monte Carlo
method. The proposed methodology is successfully applied on the Ising and the
Potts models.% where it is used to assess the performance of three state-of-the
art estimators of the parameter of these Markov random fields
Colloidal particle motion as a diagnostic of DNA conformational transitions
Tethered particle motion is an experimental technique to monitor
conformational changes in single molecules of DNA in real time, by observing
the position fluctuations of a micrometer-size particle attached to the DNA.
This article reviews some recent work on theoretical problems inherent in the
interpretation of TPM experiments, both in equilibrium and dynamical aspects.Comment: 19pp. Accepted for publication in Curr Op Colloid Interf Scienc
Distributions associated with general runs and patterns in hidden Markov models
This paper gives a method for computing distributions associated with
patterns in the state sequence of a hidden Markov model, conditional on
observing all or part of the observation sequence. Probabilities are computed
for very general classes of patterns (competing patterns and generalized later
patterns), and thus, the theory includes as special cases results for a large
class of problems that have wide application. The unobserved state sequence is
assumed to be Markovian with a general order of dependence. An auxiliary Markov
chain is associated with the state sequence and is used to simplify the
computations. Two examples are given to illustrate the use of the methodology.
Whereas the first application is more to illustrate the basic steps in applying
the theory, the second is a more detailed application to DNA sequences, and
shows that the methods can be adapted to include restrictions related to
biological knowledge.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS125 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A generalized risk approach to path inference based on hidden Markov models
Motivated by the unceasing interest in hidden Markov models (HMMs), this
paper re-examines hidden path inference in these models, using primarily a
risk-based framework. While the most common maximum a posteriori (MAP), or
Viterbi, path estimator and the minimum error, or Posterior Decoder (PD), have
long been around, other path estimators, or decoders, have been either only
hinted at or applied more recently and in dedicated applications generally
unfamiliar to the statistical learning community. Over a decade ago, however, a
family of algorithmically defined decoders aiming to hybridize the two standard
ones was proposed (Brushe et al., 1998). The present paper gives a careful
analysis of this hybridization approach, identifies several problems and issues
with it and other previously proposed approaches, and proposes practical
resolutions of those. Furthermore, simple modifications of the classical
criteria for hidden path recognition are shown to lead to a new class of
decoders. Dynamic programming algorithms to compute these decoders in the usual
forward-backward manner are presented. A particularly interesting subclass of
such estimators can be also viewed as hybrids of the MAP and PD estimators.
Similar to previously proposed MAP-PD hybrids, the new class is parameterized
by a small number of tunable parameters. Unlike their algorithmic predecessors,
the new risk-based decoders are more clearly interpretable, and, most
importantly, work "out of the box" in practice, which is demonstrated on some
real bioinformatics tasks and data. Some further generalizations and
applications are discussed in conclusion.Comment: Section 5: corrected denominators of the scaled beta variables (pp.
27-30), => corrections in claims 1, 3, Prop. 12, bottom of Table 1. Decoder
(49), Corol. 14 are generalized to handle 0 probabilities. Notation is more
closely aligned with (Bishop, 2006). Details are inserted in eqn-s (43); the
positivity assumption in Prop. 11 is explicit. Fixed typing errors in
equation (41), Example
Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery
This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive Gaussian noise. The abundance parameters appearing in this model satisfy positivity and additivity constraints. These constraints are naturally expressed in a Bayesian context by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. A Gibbs sampler allows one to draw samples distributed according to the posteriors of interest and to estimate the unknown abundances. An extension of the algorithm is finally studied for mixtures with unknown numbers of spectral components belonging to a know library. The performance of the different unmixing strategies is evaluated via simulations conducted on synthetic and real data
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