7,087 research outputs found
Joining refractory/austenitic bimetal tubing Supplemental report
Joining bimetal tubing consisting of austenitic stainless steel with inner lining of niobium or tantalu
Determination of optimum fabrication techniques for the production of austenitic refractory bimetallic tubing Midpoint report, 29 Mar. - 23 Oct. 1967
Destructive and nondestructive tests on tantalum austenitic bimetallic tubing for SNAP 8 boiler syste
Harold Jeffreys's Theory of Probability Revisited
Published exactly seventy years ago, Jeffreys's Theory of Probability (1939)
has had a unique impact on the Bayesian community and is now considered to be
one of the main classics in Bayesian Statistics as well as the initiator of the
objective Bayes school. In particular, its advances on the derivation of
noninformative priors as well as on the scaling of Bayes factors have had a
lasting impact on the field. However, the book reflects the characteristics of
the time, especially in terms of mathematical rigor. In this paper we point out
the fundamental aspects of this reference work, especially the thorough
coverage of testing problems and the construction of both estimation and
testing noninformative priors based on functional divergences. Our major aim
here is to help modern readers in navigating in this difficult text and in
concentrating on passages that are still relevant today.Comment: This paper commented in: [arXiv:1001.2967], [arXiv:1001.2968],
[arXiv:1001.2970], [arXiv:1001.2975], [arXiv:1001.2985], [arXiv:1001.3073].
Rejoinder in [arXiv:0909.1008]. Published in at
http://dx.doi.org/10.1214/09-STS284 the Statistical Science
(http://www.imstat.org/sts/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Evaluation of tantalum/316 stainless steel bimetallic tubing
Evaluation of different manufacturing of tantalum 316 stainless steel bimetallic tubin
Stochastic Development Regression on Non-Linear Manifolds
We introduce a regression model for data on non-linear manifolds. The model
describes the relation between a set of manifold valued observations, such as
shapes of anatomical objects, and Euclidean explanatory variables. The approach
is based on stochastic development of Euclidean diffusion processes to the
manifold. Defining the data distribution as the transition distribution of the
mapped stochastic process, parameters of the model, the non-linear analogue of
design matrix and intercept, are found via maximum likelihood. The model is
intrinsically related to the geometry encoded in the connection of the
manifold. We propose an estimation procedure which applies the Laplace
approximation of the likelihood function. A simulation study of the performance
of the model is performed and the model is applied to a real dataset of Corpus
Callosum shapes
A Compact Linear Programming Relaxation for Binary Sub-modular MRF
We propose a novel compact linear programming (LP) relaxation for binary
sub-modular MRF in the context of object segmentation. Our model is obtained by
linearizing an -norm derived from the quadratic programming (QP) form of
the MRF energy. The resultant LP model contains significantly fewer variables
and constraints compared to the conventional LP relaxation of the MRF energy.
In addition, unlike QP which can produce ambiguous labels, our model can be
viewed as a quasi-total-variation minimization problem, and it can therefore
preserve the discontinuities in the labels. We further establish a relaxation
bound between our LP model and the conventional LP model. In the experiments,
we demonstrate our method for the task of interactive object segmentation. Our
LP model outperforms QP when converting the continuous labels to binary labels
using different threshold values on the entire Oxford interactive segmentation
dataset. The computational complexity of our LP is of the same order as that of
the QP, and it is significantly lower than the conventional LP relaxation
Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property
The AMP Markov property is a recently proposed alternative Markov property
for chain graphs. In the case of continuous variables with a joint multivariate
Gaussian distribution, it is the AMP rather than the earlier introduced LWF
Markov property that is coherent with data-generation by natural
block-recursive regressions. In this paper, we show that maximum likelihood
estimates in Gaussian AMP chain graph models can be obtained by combining
generalized least squares and iterative proportional fitting to an iterative
algorithm. In an appendix, we give useful convergence results for iterative
partial maximization algorithms that apply in particular to the described
algorithm.Comment: 15 pages, article will appear in Scandinavian Journal of Statistic
Free energies of crystalline solids: a lattice-switch Monte Carlo method
We present a method for the direct evaluation of the difference between the
free energies of two crystalline structures, of different symmetry. The method
rests on a Monte Carlo procedure which allows one to sample along a path,
through atomic-displacement-space, leading from one structure to the other by
way of an intervening transformation that switches one set of lattice vectors
for another. The configurations of both structures can thus be sampled within a
single Monte Carlo process, and the difference between their free energies
evaluated directly from the ratio of the measured probabilities of each. The
method is used to determine the difference between the free energies of the fcc
and hcp crystalline phases of a system of hard spheres.Comment: 5 pages Revtex, 3 figure
- …