A practical method for calculating largest Lyapunov exponents

by averaging the prediction of the different models, using as weights the posterior probability of the models. However, an open problem of BMA is the choice of the prior probability of the models, which can largely impact on the inferences, especially when data are scarce. We present Credal Model Averaging

a compromise between low bias and small statistical errors, for short data series. This new estimator out-performs other currently available ones when the data sets are small and the probabilities of the possible outputs of the random variable are not close to zero. Otherwise, other well

. Although the performance as measured on the training set continues to improve as the algorithms progress, performance on related data sets may eventually begin to deteriorate. The cause of this behavior can be seen when the training problem is stated in the Alternating Minimization framework [1]. A

Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments on various datasets show that, given the same size partitions and bags, the use of disjoint

Abstract. We use decision trees and genetic algorithms to analyze the academic performance of students throughout an academic year at a distance learning university. Based on the accuracy of the generated rules, and on crossexaminations of various groups of the same student population, we surprisingly observe that students ’ performance is clustered around tutors. 1

• Open source toolkit – advances state-of-the-art of statistical machine translation models – best performance of European Parliament task – competitive on IWSLT and TC-Star • Factored models – outperform traditional phrase-based models – framework for a wide range of models – integrated approach to morphology and syntax • Confusion networks – exploit ambiguous input and outperform 1-best – enable integrated approach to speech translation

to changes in the following quantities: embedding dimension, size of data set, reconstruction delay, and noise level. Furthermore, one may use the algorithm to calculate simultaneously the correlation dimension. Thus, one sequence of computations will yield an estimate of both the level of chaos

We address the problem of improving the reliability of independence-based causal discovery algorithms that results from the execution of statistical independence tests on small data sets, which typically have low reliability. We model the problem as a knowledge base containing a set of independence

The C statistic is a commonly reported measure of screening test performance. Optimistic estimation of the C statistic is a frequent problem because of overfitting of statistical models in small data sets, and methods exist to correct for this issue. However, many studies do not use suchmethods