3,694 research outputs found
Atomic Parameters for the Transition of Ne I relevant in nuclear physics
We calculated the magnetic dipole hyperfine interaction constants and the
electric field gradients of and levels
of Ne I by using the multiconfiguration Dirac-Hartree-Fock method. The
electronic factors contributing to the isotope shifts were also estimated for
the nm transition connecting these two states. Electron
correlation and relativistic effects including the Breit interaction were
investigated in details. Combining with recent measurements, we extracted the
nuclear quadrupole moment values for Ne and Ne with a smaller
uncertainty than the current available data. Isotope shifts in the
transition based on the present
calculated field- and mass-shift parameters are in good agreement with the
experimental values. However, the field shifts in this transition are two or
three orders of magnitude smaller than the mass shifts, making rather difficult
to deduce changes in nuclear charge mean square radii. According to our
theoretical predictions, we suggest to use instead transitions connecting
levels arising from the configuration to the ground state, for which
the normal mass shift and specific mass shift contributions counteract each
other, producing relatively small mass shifts that are only one order of
magnitude larger than relatively large field shifts, especially for the
transition
A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models
For many classification and regression problems, a large number of features
are available for possible use - this is typical of DNA microarray data on gene
expression, for example. Often, for computational or other reasons, only a
small subset of these features are selected for use in a model, based on some
simple measure such as correlation with the response variable. This procedure
may introduce an optimistic bias, however, in which the response variable
appears to be more predictable than it actually is, because the high
correlation of the selected features with the response may be partly or wholely
due to chance. We show how this bias can be avoided when using a Bayesian model
for the joint distribution of features and response. The crucial insight is
that even if we forget the exact values of the unselected features, we should
retain, and condition on, the knowledge that their correlation with the
response was too small for them to be selected. In this paper we describe how
this idea can be implemented for ``naive Bayes'' models of binary data.
Experiments with simulated data confirm that this method avoids bias due to
feature selection. We also apply the naive Bayes model to subsets of data
relating gene expression to colon cancer, and find that correcting for bias
from feature selection does improve predictive performance
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