Bayesian Networks are used to model a user's behaviour. There is not much research on the use of Frequentist Inference to accomplish this same task. This paper aims to analyze and describe the differences between inference methods: Bayesian and Frequentist. A simulation was conducted using Conditional Probabilities that were drawn from the Drupal Usability Study that was conducted in 2012 to apply to both inference methods, Bayesian and Frequentist. Results from this simulation showed that for most probabilities, Bayesian and Frequentist values are reasonably close. Although more frequentist values were equal to 50% than Bayesian values. With this, it was deduced that for Adaptive User Interfaces, Bayesian Inference is a superior method to use.
This paper presents a new algorithm for a batch of task makespan minimisation in heterogeneous multigrid computing. Heterogeneous grids are known to cause straggling task problem that increases task execution makespan. Existing task distribution algorithms solve this problem by using information about the compute node capacities or task sizes. However, such information may not always be available. Task stalling solves both problems. However, this method is described for queuing systems consisting of only two heterogeneous servers or grids. Our proposed algorithm is based on an improved task stalling method, allowing it to distribute tasks in systems consisting of two or more grids. Experiment results show reduced task execution makespan by up to 19,92% compared to FIFO. This allows us to conclude that the new algorithm is suitable for a batch of task makespan minimisation in heterogeneous multigrid computing.