Toward Bayesian Data Compression

Bayesian Prediction in Spatial Data Analysis

Bayesian Compression for Deep Learning

Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack this problem is by taking a Bayesian point of view, where through sparsity inducing priors we prune large parts of the network. We introduce two novelties in this paper: 1) we use hierarchical priors to prune nodes ins...

Toward a Multi-method Approach: Lossy Data Compression for Climate Simulation Data

Earth System Model (ESM) simulations are increasingly constrained by the amount of data that they generate rather than by computational resources. The use of lossy data compression on model output can reduce storage costs and data transmission overheads, but care must be taken to ensure that science results are not impacted. Choosing appropriate compression algorithms and parameters is not triv...

Bayesian Analysis of Survival Data with Spatial Correlation

Often in practice the data on the mortality of a living unit correlation is due to the location of the observations in the study‎. ‎One of the most important issues in the analysis of survival data with spatial dependence‎, ‎is estimation of the parameters and prediction of the unknown values in known sites based on observations vector‎. ‎In this paper to analyze this type of survival‎, ‎Cox...

Bayesian Modeling Based on Data from the Internet of Things

The Internet of Things is suggested as the upcoming revolution in the Information and communication technology due to its very high capability of making various businesses and industries more productive and efficient. This productivity comes from the emergence of innovation and the introduction of new capabilities for businesses. Different industries have shown varying reactions to IOT, but wha...