I’m writing my first column for the 2002 as chair of computing, although we have already met in these columns as I have been editor for the section for more than a year now. As I scramble to find time to do my own research on the bootstrap and its applications to biology, in particular the problem of phylogenetic trees, I am confronted more and more by the digital divide that separates me from ...

Descriptive statistics is the process of summarizing gathered raw data from a research and creating useful statistics, which help the better understanding of data. According to the types of variables, which consist of qualitative and quantitative variables, some descriptive statistics have been introduced. Frequency percentage is used in qualitative data, and mean, median, mode, standard deviat...

An intuitive measure of association between two multivariate data sets can be defined as the maximal value that a bivariate association measure between any one-dimensional projections of each data set can attain. Rank correlation measures thereby have the advantage that they combine good robustness properties with good efficiency. The software package ccaPP provides fast implementations of such...

Change point (CP) detection is an important problem in data mining (DM) applications. We consider this problem solving in multi-agent systems (MAS) domains. Change point testing allows agents to recognize changes in the environment, to detect more accurately current state information and provide more appropriate information for decision-making. Standard statistical procedures for change point d...

Conditioning on the observed data is an important and flexible design principle for statistical test procedures. Although generally applicable, permutation tests currently in use are limited to the treatment of special cases, such as contingency tables or K-sample problems. A new theoretical framework for permutation tests opens up the way to a unified and generalized view. We argue that the tr...

If, in the mid 1980’s, one had asked the average statistician about the difficulties of using Bayesian Statistics, his/her most likely answer would have been “Well, there is this problem of selecting a prior distribution and then, even if one agrees on the prior, the whole Bayesian inference is simply impossible to implement in practice!” The same question asked in the 21th Century does not pro...

A direct numerical simulation (DNS) of fully developed turbulent pipe flow is performed at Reτ ≈ 170 and 500 to examine the effect of the streamwise domain length on the convergence of turbulence statistics. Computational domain lengths vary from the πδ to 20πδ. Lower order statistics such as mean flow, turbulence intensities, Reynolds stress, correlations and higher order statistics including ...

Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Both the accuracy and computational efficiency of ABC depend on the choice of summary statistic, but outside of special cases where the optimal summary statistics are known, it is unclear which guiding principles can be used to constru...

We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. The method is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors. Properties of the posterior distribution of a parameter, such as its mean or density curve, are approximated without explicit likelihood calculations. T...