33,133 research outputs found
Uncertainty Updating in the Description of Coupled Heat and Moisture Transport in Heterogeneous Materials
To assess the durability of structures, heat and moisture transport need to
be analyzed. To provide a reliable estimation of heat and moisture distribution
in a certain structure, one needs to include all available information about
the loading conditions and material parameters. Moreover, the information
should be accompanied by a corresponding evaluation of its credibility. Here,
the Bayesian inference is applied to combine different sources of information,
so as to provide a more accurate estimation of heat and moisture fields [1].
The procedure is demonstrated on the probabilistic description of heterogeneous
material where the uncertainties consist of a particular value of individual
material characteristic and spatial fluctuations. As for the heat and moisture
transfer, it is modelled in coupled setting [2]
A program for the Bayesian Neural Network in the ROOT framework
We present a Bayesian Neural Network algorithm implemented in the TMVA
package, within the ROOT framework. Comparing to the conventional utilization
of Neural Network as discriminator, this new implementation has more advantages
as a non-parametric regression tool, particularly for fitting probabilities. It
provides functionalities including cost function selection, complexity control
and uncertainty estimation. An example of such application in High Energy
Physics is shown. The algorithm is available with ROOT release later than 5.29.Comment: 12 pages, 6 figure
Interpreting large-scale redshift-space distortion measurements
The simplest theory describing large-scale redshift-space distortions (RSD),
based on linear theory and distant galaxies, depends on the growth of
cosmological structure, suggesting that strong tests of General Relativity can
be constructed from galaxy surveys. As data sets become larger and the expected
constraints more precise, the extent to which the RSD follow the simple theory
needs to be assessed in order that we do not introduce systematic errors into
the tests by introducing inaccurate simplifying assumptions. We study the
impact of the sample geometry, non-linear processes, and biases induced by our
lack of understanding of the radial galaxy distribution on RSD measurements.
Using LasDamas simulations of the Sloan Digital Sky Survey II (SDSS-II)
Luminous Red Galaxy (LRG) data, these effects are shown to be important at the
level of 20 per cent. Including them, we can accurately model the recovered
clustering in these mock catalogues on scales 30 -- 200 Mpc/h. Applying this
analysis to robustly measure parameters describing the growth history of the
Universe from the SDSS-II data, gives
and
when no prior is imposed on the
growth-rate, and the background geometry is assumed to follow a CDM
model with the WMAP + SNIa priors. The standard WMAP constrained CDM
model with General Relativity predicts
and
, which is fully consistent with
these measurements.Comment: 20 pages, 17 figures, 1 tabl
Fitting Parton Distribution Data with Multiplicative Normalization Uncertainties
We consider the generic problem of performing a global fit to many
independent data sets each with a different overall multiplicative
normalization uncertainty. We show that the methods in common use to treat
multiplicative uncertainties lead to systematic biases. We develop a method
which is unbiased, based on a self--consistent iterative procedure. We
demonstrate the use of this method by applying it to the determination of
parton distribution functions with the NNPDF methodology, which uses a Monte
Carlo method for uncertainty estimation.Comment: 33 pages, 5 figures: published versio
Nonparametric Methods in Astronomy: Think, Regress, Observe -- Pick Any Three
Telescopes are much more expensive than astronomers, so it is essential to
minimize required sample sizes by using the most data-efficient statistical
methods possible. However, the most commonly used model-independent techniques
for finding the relationship between two variables in astronomy are flawed. In
the worst case they can lead without warning to subtly yet catastrophically
wrong results, and even in the best case they require more data than necessary.
Unfortunately, there is no single best technique for nonparametric regression.
Instead, we provide a guide for how astronomers can choose the best method for
their specific problem and provide a python library with both wrappers for the
most useful existing algorithms and implementations of two new algorithms
developed here.Comment: 19 pages, PAS
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