3,701 research outputs found
Parallel Multivariate Meta-Theorems
Fixed-parameter tractability is based on the observation that many hard problems become tractable even on large inputs as long as certain input parameters are small. Originally, "tractable" just meant "solvable in polynomial time," but especially modern hardware raises the question of whether we can also achieve "solvable in polylogarithmic parallel time." A framework for this study of parallel fixed-parameter tractability is available and a number of isolated algorithmic results have been obtained in recent years, but one of the unifying core tools of classical FPT theory has been missing: algorithmic meta-theorems. We establish two such theorems by giving new upper bounds on the circuit depth necessary to solve the model checking problem for monadic second-order logic, once parameterized by the tree width and the formula (this is a parallel version of Courcelle\u27s Theorem) and once by the tree depth and the formula. For our proofs we refine the analysis of earlier algorithms, especially of Bodlaender\u27s, but also need to add new ideas, especially in the context where the parallel runtime is bounded by a function of the parameter and does not depend on the length of the input
A method of moments estimator of tail dependence
In the world of multivariate extremes, estimation of the dependence structure
still presents a challenge and an interesting problem. A procedure for the
bivariate case is presented that opens the road to a similar way of handling
the problem in a truly multivariate setting. We consider a semi-parametric
model in which the stable tail dependence function is parametrically modeled.
Given a random sample from a bivariate distribution function, the problem is to
estimate the unknown parameter. A method of moments estimator is proposed where
a certain integral of a nonparametric, rank-based estimator of the stable tail
dependence function is matched with the corresponding parametric version. Under
very weak conditions, the estimator is shown to be consistent and
asymptotically normal. Moreover, a comparison between the parametric and
nonparametric estimators leads to a goodness-of-fit test for the semiparametric
model. The performance of the estimator is illustrated for a discrete spectral
measure that arises in a factor-type model and for which likelihood-based
methods break down. A second example is that of a family of stable tail
dependence functions of certain meta-elliptical distributions.Comment: Published in at http://dx.doi.org/10.3150/08-BEJ130 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Wishart distributions for decomposable graphs
When considering a graphical Gaussian model Markov with
respect to a decomposable graph , the parameter space of interest for the
precision parameter is the cone of positive definite matrices with fixed
zeros corresponding to the missing edges of . The parameter space for the
scale parameter of is the cone , dual to , of
incomplete matrices with submatrices corresponding to the cliques of being
positive definite. In this paper we construct on the cones and two
families of Wishart distributions, namely the Type I and Type II Wisharts. They
can be viewed as generalizations of the hyper Wishart and the inverse of the
hyper inverse Wishart as defined by Dawid and Lauritzen [Ann. Statist. 21
(1993) 1272--1317]. We show that the Type I and II Wisharts have properties
similar to those of the hyper and hyper inverse Wishart. Indeed, the inverse of
the Type II Wishart forms a conjugate family of priors for the covariance
parameter of the graphical Gaussian model and is strong directed hyper Markov
for every direction given to the graph by a perfect order of its cliques, while
the Type I Wishart is weak hyper Markov. Moreover, the inverse Type II Wishart
as a conjugate family presents the advantage of having a multidimensional shape
parameter, thus offering flexibility for the choice of a prior.Comment: Published at http://dx.doi.org/10.1214/009053606000001235 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Bayesian optimization usually assumes that a Bayesian prior is given.
However, the strong theoretical guarantees in Bayesian optimization are often
regrettably compromised in practice because of unknown parameters in the prior.
In this paper, we adopt a variant of empirical Bayes and show that, by
estimating the Gaussian process prior from offline data sampled from the same
prior and constructing unbiased estimators of the posterior, variants of both
GP-UCB and probability of improvement achieve a near-zero regret bound, which
decreases to a constant proportional to the observational noise as the number
of offline data and the number of online evaluations increase. Empirically, we
have verified our approach on challenging simulated robotic problems featuring
task and motion planning.Comment: Proceedings of the Thirty-second Conference on Neural Information
Processing Systems, 201
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