Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed....

Bayesian classifier has gained wide popularity as a probability-based classification method despite its assumption that attributes are conditionally mutually independent given the class label. This paper makes a study into various algorithms to improve the classification accuracy of Bayesian methods with respect to real estate datasets. We have applied Bayesian methods on two variations of data...

We present distribution-free generalization error bounds which apply to a wide class of approximate Bayesian Gaussian process classification (GPC) techniques, powerful nonparametric learning methods similar to Support Vector machines. The bounds use the PACBayesian theorem [8] for which we provide a simplified proof, leading to new insights into its relation to traditional VC type union bound t...

given the sensitivity of current ground-based gravitational wave (gw) detectors, any continuous-wave signal we can realistically expect will be at a level or below the background noise. hence, any data analysis of detector data will need to rely on statistical techniques to separate the signal from the noise. while with the current sensitivity of our detectors we do not expect to detect any tru...

Bayesian modelling and variational inference are rooted in Bayesian statistics, and easily benefit from the vast literature in the field. In contrast, deep learning lacks a solid mathematical grounding. Instead, empirical developments in deep learning are often justified by metaphors, evading the unexplained principles at play. It is perhaps astonishing then that most modern deep learning model...

animal models are used to model the observations of animal performance that are genetically dependent.these models are considered as generalized linear mixed models and the genetic correlation structure of data is considered through random effects of breeding values. one goal of the mentioned models is to estimate variance components. in this research, an approximate bayesian approach presented...

A series of monte carlo studies were performed to as­ sess the extent to which different inference procedures robustly output reasonable belief values in the context of increasing levels of judgmental imprecision. It was found that, when com­ pared to an equal-weights linear model, the Bayesian procedures are more likely to deduce strong support for a hypothesis. But, the Bayesian procedures ar...

This paper describes the use of a Bayesian network to resolve anaphora by probabilistically combining linguistic evidence. By adopting a Bayesian approach, we are able to combine diverse evidence in a principled way, extend current understanding of linguistic phenomena by quantifying relationships empirically, and better model the non-deterministic role of linguistic evidence in resolution of a...

There has been increasing interest in applying Bayesian techniques for model selection (such as Bayesian Evidence) to problems in cosmology. A typical example is in assessing whether observational data favour a cosmological constant over evolving dark energy. In this paper, the example of dark energy is used to illustrate limitations in the application of Bayesian Evidence associated with subje...