Suchergebnisse

Linear and log-linear pools are widely used methods for aggregating expert belief. This paper frames the expert aggregation problem as a decision problem with scoring rules. We propose a scoring function that uses the Kullback-Leibler (KL) divergence measure between the aggregate distribution and each of the expert distributions. The asymmetric nature of the KL measure allows for a convenient scoring system for which the linear and log-linear pools provide the optimal assignment. We also propose a "goodness-of-fit" measure that determines how well each opinion pool characterizes its expert distributions, and also determines the performance of each pool under this scoring function. We work through several examples to illustrate the approach.

Cross entropy and Kullback–Leibler (K-L) divergence are fundamental quantities of information theory, and they are widely used in many fields. Since cross entropy is the negated logarithm of likelihood, minimizing cross entropy is equivalent to maximizing likelihood, and thus, cross entropy is applied for optimization in machine learning. K-L divergence also stands independently as a commonly used metric for measuring the difference between two distributions. In this paper, we introduce new inequalities regarding cross entropy and K-L divergence by using the fact that cross entropy is the negated logarithm of the weighted geometric mean. We first apply the well-known rearrangement inequality, followed by a recent theorem on weighted Kolmogorov means, and, finally, we introduce a new theorem that directly applies to inequalities between K-L divergences. To illustrate our results, we show numerical examples of distributions ; Mateu Sbert acknowledges the funding of National Natural Science Foundation of China under grants No.61471261 and No.61771335, and by grant TIN2016-75866-C3-3-R from Spanish Government, Jordi Poch and Anton Bardera acknowledge the funding of TIN2016-75866-C3-3-R from Spanish Government

We describe an information-theoretic paradigm for analysis of ecological data,
based on Kullback–Leibler information, that is an extension of
likelihood theory and avoids the pitfalls of null hypothesis testing.
Information-theoretic approaches emphasise a deliberate focus on the
a priori science in developing a set of multiple working
hypotheses or models. Simple methods then allow these hypotheses (models) to
be ranked from best to worst and scaled to reflect a strength of evidence
using the likelihood of each model
(gi), given the data and the
models in the set (i.e.
L(gi
| data)). In addition, a variance component due
to model-selection uncertainty is included in estimates of precision. There
are many cases where formal inference can be based on all the models in the
a priori set and this multi-model inference represents a
powerful, new approach to valid inference. Finally, we strongly recommend
inferences based on a priori considerations be carefully
separated from those resulting from some form of data dredging. An example is
given for questions related to age- and sex-dependent rates of tag loss in
elephant seals (Mirounga leonina).

This article considers the problem of estimating the cell frequencies in a contingency table under inequality constraints. Algorithms are proposed for cell frequency estimation via minimizing the Kullback–Leibler distance subject to inequality constraints. The proposed algorithms are shown to be simple, easy to be used, fast, and reliable. Theorems are derived to guarantee the convergence of the algorithms. Applications and extensions of the algorithms are provided for more general problems than contingency table. The R programs that implement the proposed algorithms are presented in Appendix B.

Proceedings of: IberSPEECH 2012 Conference, Madrid, Spain, November 21-23, 2012. ; A speech denoising method based on Non-Negative Matrix Factorization (NMF) is presented in this paper. With respect to previous related works, this paper makes two contributions. First, our method does not assume a priori knowledge about the nature of the noise. Second, it combines the use of the Kullback-Leibler divergence with sparseness constraints on the activation matrix, improving the performance of similar techniques that minimize the Euclidean distance and/or do not consider any sparsification. We evaluate the proposed method for both, speech enhancement and automatic speech recognitions tasks, and compare it to conventional spectral subtraction, showing improvements in speech quality and recognition accuracy, respectively, for different noisy conditions. ; This work has been partially supported by the Spanish Government grants TSI-020110-2009-103 and TEC2011-26807. ; Publicado

Engine misfire detection is an important part of the On-Board Diagnostics (OBDII) legislations to reduce exhaust emissions and avoid damage to the catalytic converters. The flywheel angular velocity signal is analyzed, investigating how to use the signal in order to best detect misfires. An algorithm for engine misfire detection is proposed based on the flywheel angular velocity signal. The flywheel signal is used to estimate the torque at the flywheel and a test quantity is designed by weighting and thresholding the samples of estimated torque related to one combustion. During the development process, the Kullback-Leibler divergence is used to analyze the ability to detect a misfire given a test quantity and how the misfire detectability performance varies depending on, e.g., load and speed. The Kullback-Leibler divergence is also used for parameter optimization to maximize the difference between misfire data and fault-free data. Evaluation shows that the proposed misfire detection algorithm is able to have a low probability of false alarms while having a low probability of missed detections.