Ransomware is a dangerous malware that blocks access to data through encryption, and it exploits device vulnerabilities perform chain attacks from one system another. This study results in modeling the threat of ransomware using Bayesian Network. The structure model created can be exploited. As basis for calculating probability model, EPSS vulnerability score used. risk exposure rating calculat...

1) Species distribution models (SDMs) are currently the main tools to derive species niche estimates and spatially explicit predictions for geographical distribution. However, unobserved environmental conditions ecological processes may confound model if they have direct impact on and, at same time, correlated with observed covariates. This, so-called spatial confounding, is a general property ...

Bowers and Davis (2012) criticize Bayesian modelers for telling "just so" stories about cognition and neuroscience. Their criticisms are weakened by not giving an accurate characterization of the motivation behind Bayesian modeling or the ways in which Bayesian models are used and by not evaluating this theoretical framework against specific alternatives. We address these points by clarifying o...

Bayesian statistics provides a general framework for the treatment of problems involving uncertainty. A brief introduction to the principles of Bayesian analysis is presented. Special attention is given to the application of the Bayesian viewpoint in deterministic settings. Short reviews of the roles the Bayesian approach plays in areas of interpolation or \objective analysis" and data assimila...

Over the last decade, the Bayesian approach has increased in popularity in many application areas. It uses a probabilistic framework which encodes our beliefs or actions in situations of uncertainty. Information from several models can also be combined based on the Bayesian framework to achieve better inference and to better account for modeling uncertainty. The approach we adopted here is to u...