A Feature-Based Bayesian Method for Content Popularity Prediction in Edge-Caching Networks

Edge-caching is recognized as an efficient technique for future wireless cellular networks to improve network capacity and user-perceived quality of experience. Due to the random content requests and the limited cache memory, designing an efficient caching policy is a challenge. To enhance the performance of caching systems, an accurate content request prediction algorithm is essential. Here, we introduce a flexible model, a Poisson regressor based on a Gaussian process, for the content request distribution in stationary environments. Our proposed model can incorporate the content features as side information for prediction enhancement. In order to learn the model parameters, which yield the Poisson rates or alternatively content popularities, we invoke the Bayesian approach which is very robust against over-fitting. However, the posterior distribution in the Bayes formula is analytically intractable to compute. To tackle this issue, we apply a Monte Carlo Markov Chain (MCMC) method to approximate the posterior distribution. Two types of predictive distributions are formulated for the requests of existing contents and for the requests of a newly-added content. Finally, simulation results are provided to confirm the accuracy of the developed content popularity learning approach.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0306

System Reliability Estimation of Divert Attitude Control System of a Launch Vehicle using Bayesian Networks

Divert attitude and control system (DACS) is a one-shot system and provides attitude correction and translation of the Launch vehicle. DACS consists of many flight critical sub systems which are arranged in a series configuration. The traditional Reliability block diagram and Fault tree diagram methods are unsuitable for reliability modelling, when considering uncertainty among the components and system. Bayesian network is the natural choice to model dependencies among the components and system. DACS being one shot system, it is very expensive and time consuming to test more number of systems during the design and development. Hence the data is drawn from component level, subsystem level and expert opinion is used for reliability estimation. In this paper, Bayesian network modelling of DAC system was carried out for estimating the reliability using multi-level data. An algorithm is developed for computation of Conditional probabilities in Bayesian network. Posterior probability distribution of components is calculated using Markov Chain Monte Carlo (MCMC) simulations and results are compared with Junction tree based exact inference algorithm. MATLAB code is developed to estimate the reliability of DAC system

Improving Bayesian statistics understanding in the age of Big Data with the bayesvl R package

The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines the ability to construct Bayesian network models using directed acyclic graphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of the ggplot2 package. As a result, it can improve the user experience and intuitive understanding when constructing and analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the package for Big Data analytics and cognitive computing