In this paper, a full Bayesian weighted probabilistic model is developed for random
classification trees. The new model Bayesian Weighted Random Classification Forest …

Random forest (RF) is a widely used data prediction and variable selection technique.
However, the variable selection aspect of RF can become unreliable when there are more …

In this paper, the one-way ANOVA model and its application in Bayesian multi-class variable
selection is considered. A full Bayesian bootstrap prior ANOVA test function is developed …

A new Bayesian estimation procedure for extended cox model with time varying covariate
was presented. The prior was determined using bootstrapping technique within the …

In this study, the Variational Bayes (VB) approach was hybridized with the bootstrap prior
procedure to improve the accuracy of subset selection as well as optimizing the algorithm …

Internet traffic data such as the number of transmitted packets and time spent on the
transmission of Internet protocols (IPs) have been shown to exhibit self-similar property …

This paper presents methods of Bayesian inference for Random Forest (RF) procedure with
high-dimensional data. The new methods termed Bayesian Random Forest (BRF) is …

Asymptotic theories for fractional cointegrations have been extensively studied in the context
of time series data, with numerous empirical studies and tests having been developed …

An important aspect of improving software system is testing. However, it is time demanding
and sometimeslabour intensive if done manually. In this paper, we developed an automatic …

The Weibull, log-logistic and log-normal distributions represent the heavy-tailed distributions
that are often used in modelling time-to-event data. While the loglogistic and log-normal …