the logistic, gompertz, richards and asymmetric logistic growth curve models were fitted to body weight data of local ghanaian chickens and french sasso t44 chickens. all four growth models provided good fit for each sex by genotype growth data with r2 values ranging from 86.7% to 96.7%. the rate constant parameter, k, ranged between 0.137 and 0.271 and were significantly different from zero fo...

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps the process of prediction. Most work present defect prediction focused on selection best techniques. For this purpose, deep learning ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning training data par...

This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion)...

A two component parametric mixture is proposed to model survival after an invasive treatment, when patients may experience different hazards regimes: a risk of early mortality directly related to the treatment and/or the treated condition, and a risk of late death influenced by several exogenous factors. The parametric mixture is based on Weibull distributions for both components. Different set...

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...

Latent tree (LT) models are a special class of Bayesian networks that can be used for cluster analysis, latent structure discovery and density estimation. A number of search-based algorithms for learning LT models have been developed. In particular, the HSHC algorithm by [1] and the EAST algorithm by [2] are able to deal with data sets with dozens to around 100 variables. Both HSHC and EAST aim...