Over the last two decades a large number of algorithms has been developed for regulatory motif finding. Here we show how many of these algorithms, especially those that model binding specificities of regulatory factors with position specific weight matrices (WMs), naturally arise within a general Bayesian probabilistic framework. We discuss how WMs are constructed from sets of regulatory sites,...

A well studied procedure for estimating a parameter from observed data is to maximize the likelihood function. When a maximizer cannot be obtained in closed form, iterative maximization algorithms, such as the expectation maximization (EM) maximum likelihood algorithms, are needed. The standard formulation of the EM algorithms postulates that finding a maximizer of the likelihood is complicated...

This paper develops a new technique for estimating mixed logit models with a simple minorization-maximization (MM) algorithm. The algorithm requires minimal coding and is easy to implement for a variety of mixed logit models. Most importantly, the algorithm has a very low cost per iteration relative to current methods, producing substantial computational savings. In addition, the method is asym...

Sequence detection is studied for communication channels with intersymbol interference and non-Gaussian noise using a novel adaptive receiver structure. The receiver adapts itself to the noise environment using an algorithm which employs a Gaussian mixture distribution model and the expectation maximization algorithm. Two alternate procedures are studied for sequence detection. These are a proc...

Recently, analysis of structural equation models with polytomous and continuous variables has received a lot of attention. However, contributions to the selection of good models are limited. The main objective of this article is to investigate the maximum likelihood estimation of unknown parameters in a general LISREL-type model with mixed polytomous and continuous data and propose a model sele...

Article history: Received 7 February 2011 Accepted 24 October 2011 Available online xxxx

Learning ontology from unstructured text is a challenging task. Over the years, a lot of research has been done to predict ontological relation between a pair of concepts. However all these measures predict relation with a varying degree of accuracy. There has also been work on learning ontology by combining evidences from heterogeneous sources, but most of these algorithms are ad hoc in nature...

Although on-line measurements play a ®ital role in process control and monitoring ( process performance, they are corrupted by noise and occasional outliers such as noise ) spikes . Thus, there is a need to rectify the data by remo®ing outliers and reducing noise effects. Well-known techniques such as Kalman Filtering ha®e been used effecti®ely to filter noise measurements, but it is not design...