203,787 research outputs found
Mutual independence of critical temperature and superfluid density under pressure in optimally electron-doped superconducting LaFeAsOF
The superconducting properties of LaFeAsOF in conditions of
optimal electron-doping are investigated upon the application of external
pressure up to kbar. Measurements of muon-spin spectroscopy and dc
magnetometry evidence a clear mutual independence between the critical
temperature and the low-temperature saturation value for the ratio
(superfluid density over effective band mass of Cooper pairs).
Remarkably, a dramatic increase of % is reported for at
the maximum pressure value while is substantially unaffected in the
whole accessed experimental window. We argue and demonstrate that the
explanation for the observed results must take the effect of non-magnetic
impurities on multi-band superconductivity into account. In particular, the
unique possibility to modify the ratio between intra-band and inter-bands
scattering rates by acting on structural parameters while keeping the amount of
chemical disorder constant is a striking result of our proposed model.Comment: 8 pages (Main text: 5 pages. Paper merged with supplemental
information), 5 figure
Mutual Fund Performance with Learning Across Funds
This paper is based on the premise that knowledge about the alphas of one set of funds will influence an investor's beliefs about other funds. This will be true insofar as an investor's expectation about the performance of a fund is partly a belief about the abilities of mutual fund managers as a group and, more generally, a belief about the degree to which financial markets are efficient. We develop a simple framework for incorporating this prior dependence' and find that it can have a substantial impact on the cross-section of posterior beliefs about fund performance as well as asset allocation. Under independence, the maximum posterior mean alpha increases without bound as the number of funds increases and 'extremely large' estimates are randomly observed. This is true even when fund managers have no skill. In contrast, with prior dependence, investors aggregate information across funds to form a general belief about the potential for abnormal performance. Each fund's alpha estimate is shrunk toward the aggregate estimate, mitigating extreme views. An additional implication is that restricting the estimation to surviving funds, a common practice in this literature, imparts an upward bias to the average fund alpha.
On the Optimal Transmission of Non-Gaussian Signals through a Noisy Channel with Feedback
This paper is concerned with the optimal transmission of a non-Gaussian signal (a non-Gaussian message) through a channel with Gaussian white noise by a cording which is linear in the signal. Under the assumptions of square integrability on the signal and the independence between the signal and the noise, it will be shown that the optimal cording which maximizes the mutual information between the signal (the non-Gaussian message) and the observation process (the channel output) is to generate the estimation error process multiplied by a deterministic coefficient so that the mean power of the encorded signal takes the maximum admissible value. The result shows that the optimal transmission is such that the channel output becomes the innovations process
An information theory for preferences
Recent literature in the last Maximum Entropy workshop introduced an analogy
between cumulative probability distributions and normalized utility functions.
Based on this analogy, a utility density function can de defined as the
derivative of a normalized utility function. A utility density function is
non-negative and integrates to unity. These two properties form the basis of a
correspondence between utility and probability. A natural application of this
analogy is a maximum entropy principle to assign maximum entropy utility
values. Maximum entropy utility interprets many of the common utility functions
based on the preference information needed for their assignment, and helps
assign utility values based on partial preference information. This paper
reviews maximum entropy utility and introduces further results that stem from
the duality between probability and utility
A Class of Models for Uncorrelated Random Variables
We consider the class of multivariate distributions that gives the distribution of the sum of uncorrelated random variables by the product of their marginal distributions. This class is defined by a representation of the assumption of sub-independence, formulated previously in terms of the characteristic function and convolution, as a weaker assumption than independence for derivation of the distribution of the sum of random variables. The new representation is in terms of stochastic equivalence and the class of distributions is referred to as the summable uncorrelated marginals (SUM) distributions. The SUM distributions can be used as models for the joint distribution of uncorrelated random variables, irrespective of the strength of dependence between them. We provide a method for the construction of bivariate SUM distributions through linking any pair of identical symmetric probability density functions. We also give a formula for measuring the strength of dependence of the SUM models. A final result shows that under the condition of positive or negative orthant dependence, the SUM property implies independence
Bayesian Network Structure Learning with Permutation Tests
In literature there are several studies on the performance of Bayesian
network structure learning algorithms. The focus of these studies is almost
always the heuristics the learning algorithms are based on, i.e. the
maximisation algorithms (in score-based algorithms) or the techniques for
learning the dependencies of each variable (in constraint-based algorithms). In
this paper we investigate how the use of permutation tests instead of
parametric ones affects the performance of Bayesian network structure learning
from discrete data. Shrinkage tests are also covered to provide a broad
overview of the techniques developed in current literature.Comment: 13 pages, 4 figures. Presented at the Conference 'Statistics for
Complex Problems', Padova, June 15, 201
On a comparative study between dependence scales determined by linear and non-linear measures
In this manuscript we present a comparative study about the determination of
the relaxation (\textit{i.e.}, independence) time scales obtained from the
correlation function, the mutual information, and a criterion based on the
evaluation of a nonextensive generalisation of mutual entropy. Our results show
that, for systems with a small degree of complexity, standard mutual
information and the criterion based on its nonextensive generalisation provide
the same scale, whereas for systems with a higher complex dynamics the standard
mutual information presents a time scale consistently smaller.Comment: 14 pages. To appear in Physica
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