This paper explores a relationship between parameters for the context tree weighting and weights for a general model weighting technique. In particular, an algorithm is proposed that approximately computes the parameters from the weights, and a condition under which no error for the approximation occurs is derived. key words: model weighting technique, tree source, context tree weighting.

BACKGROUND AND OBJECTIVES Respondent-driven sampling (RDS) is widely used to sample hidden populations and RDS data are analyzed using specially designed RDS analysis tool (RDSAT). RDSAT estimates parameters such as proportions. Analysis with RDSAT requires separate weight assignment for individual variables even in a single individual; hence, regression analysis is a problem. RDS-analyst is an...

Empirical studies have shown that decision makers do not usually treat probabilities linearly. Instead, people tend to overweight small probabilities and underweight large probabilities. One way to model such distortions in decision making under risk is through a probability weighting function. We present a nonparametric estimation procedure for assessing the probability weighting function and ...

Acquisition-weighting improves the localization of MRI experiments. An approach to acquisition-weighting in a purely phase-encoded experiment is presented that is based on a variation of the sampling density in k-space. In contrast to conventional imaging or to accumulation-weighting, where k-space is sampled with uniform increments, density-weighting varies the distance between neighboring sam...

Many of the conventional similarity methods assume that molecular fragments that do not relate to biological activity carry the same weight as the important ones. One possible approach to this problem is to use the Bayesian inference network (BIN), which models molecules and reference structures as probabilistic inference networks. The relationships between molecules and reference structures in...

In this paper, we propose a method how to construct density weighting functions from Copulas. The notion of Copula was introduced by A. Sklar in 1959. A Copula is a dependence function to construct a bivariate distribution function that links joint distributions to their marginals. Other forms of dependence function, based on density weighing functions, have also been developed. The proposed me...

Hypothesis weighting is a powerful approach for improving the power of data analyses that employ multiple testing. However, in general it is not evident how to choose the weights in a data-dependent manner. We describe independent hypothesis weighting (IHW), a method for making use of informative covariates that are independent of the test statistic under the null, but informative of each test’...