In many problems in the area of artificial intelligence, it is necessary to deal with uncertainty. Using probabilistic models can also improve efficiency of standard AI-based techniques. Commonly used methods for dealing with uncertainty include Bayesian models, which can be used to describe and work with probabilistic systems effectively. This article reviews several models based on the Bayesi...

Duda, Hart, and Nilsson [ 1] have set forth a method for rule-based inference systems to use in updating the probabilities of hypotheses on the basis of multiple items of new evidence. Pednault, Zucker, and Muresan [2] claimed to give condi­ tions under which independence assumptions made by Duda et al. preclude updating-that is, prevent the evidence from altering the probabilities of the hypot...

This paper describes a number of techniques for language verification based on acoustic processing and n-gram language modelling. A new technique is described which uses anti-models to model the general class of languages. These models are then used to normalise the acoustic score giving a 34% reduction in the error rate of the system. An approach to automatically generate discriminative subwor...

The frequentest approach is the classical approach to parameter estimation. It assumes that there is an unknown but objectively fixed parameter θ [3]. It chooses the value of θ which maximizes the likelihood of observed data [4], in other words, making the available data as likely as possible. A common example is the maximum likelihood estimator (MLE). The frequentest approach is statistically ...

The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine learning, with substantial recent developments on adaptive, flexibl...

This Master’s thesis is mostly focused on Bayesian methods for the selection and testing of discrete mixture models. The main problem that is studied in the project is the analysis of data sets of several categorical variables (e.g. test items, symptoms, genes) collected on a set of subjects. We fit a discrete mixture model to the data which means that the dependencies among the different varia...

the problem of hypothesis testing with a nuisance parameter is considered. two methods forusing fuzzy knowledge on the nuisance parameter to test hypotheses are suggested. these methods areneither a pure classical nor a pure bayesian approach to hypothesis testing, but rather related to both. afew known examples and their applications, which cannot be studied by the parametric statisticalmethod...