Online Learning with Bayesian Classification Trees Supplementary Material

Dynamic trees for online analysis of massive data

Data collection at a massive scale is becoming ubiquitous in a wide variety of settings, from vast offline databases to online real-time information. Learning algorithms deployed in such contexts must rely on single-pass inference, where the data history is never revisited. Although rapidly developing, online nonparametric Bayesian inference remains challenged by massive, transient data, as the...

Predicting and understanding the stability of G-quadruplexes

MOTIVATION G-quadruplexes are stable four-stranded guanine-rich structures that can form in DNA and RNA. They are an important component of human telomeres and play a role in the regulation of transcription and translation. The biological significance of a G-quadruplex is crucially linked with its thermodynamic stability. Hence the prediction of G-quadruplex stability is of vital interest. RE...

Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling ( Supplementary Material )

Accelerating Bayesian inference for evolutionary biology models

Motivation Bayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data is pushing the limits of the current use of these methods. Results We present a parallel Metropo...

Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection

Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...