With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet e...

Two families comprising six simple schemata, which reproduce with high accuracy the outputs of Bayesian belief networks as used in actual digital forensic investigations, are described, analyzed, and evaluated numerically. Their application as a forensic triage measure to help in deciding whether a full digital forensic investigation is warranted is critically discussed. The concept of a digita...

This study presents a new simple approach for combining empirical with raw (i.e., not bias corrected) coupled model ensemble forecasts in order to make more skillful interval forecasts of ENSO. A Bayesian normal model has been used to combine empirical and raw coupled model December SST Niño-3.4 index forecasts started at the end of the preceding July (5-month lead time). The empirical forecast...

The work of Hsu & Chater [1] and Perfors et al. [2] establishes that sophisticated statistical learning techniques known as Hierarchical Bayesian Models (HBMs) can successfully capture certain observed patterns of both underand over-generalization in child language acquisition. This paper shows that a much simpler method, maximum likelihood estimation (MLE), can equal HBM performance. The work ...

Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...

We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we develop simple methods for generating priors for Bayesian-network parameters. Our work is a generalization of previous work that has concentrated on Bayesian networks containing only discrete variables and (to a lesser extent) on Gaussian networks. We introduc...