MOTIVATION Gibbs sampling has become a method of choice for the discovery of noisy patterns, known as motifs, in DNA and protein sequences. Because handling noise in microarray data presents similar challenges, we have adapted this strategy to the biclustering of discretized microarray data. RESULTS In contrast with standard clustering that reveals genes that behave similarly over all the con...

Protein backbones have characteristic secondary structures, including alpha-helices and beta-sheets. Which structure is adopted locally is strongly biased by the local amino acid sequence of the protein. Accurate (probabilistic) mappings from sequence to structure are valuable for both secondary-structure prediction and protein design. For the case of alpha-helix caps, we test whether the infor...

First-order probabilistic models combine the power of first-order logic, the de facto tool for handling relational structure, with probabilistic graphical models, the de facto tool for handling uncertainty. Lifted probabilistic inference algorithms for them have been the subject of much recent research. The main idea in these algorithms is to improve the accuracy and scalability of existing gra...

Probabilistic analogues of regular and context-free grammars are wellknown in computational linguistics, and currently the subject of intensive research. To date, however, no satisfactory probabilistic analogue of attribute-value grammars has been proposed: previous attempts have failed to define a correct parameter-estimation algorithm. In the present paper, I define stochastic attribute-value...

Finding short patterns with residue variation in a set of sequences is still an open problem in genetics, since motif-finding techniques on DNA and protein sequences are inconclusive on real data sets and their performance varies on different species. Hence, finding new algorithms and evolving established methods are vital to further understanding of genome properties and the mechanisms of prot...

This paper proposes a new algorithm for Bayesian model determination in Gaussian graphical models under G-Wishart prior distributions. We first review recent development in sampling from G-Wishart distributions for given graphs, with a particular interest in the efficiency of the block Gibbs samplers and other competing methods. We generalize the maximum clique block Gibbs samplers to a class o...

The K-Means and EM algorithms are popular in clustering and mixture modeling due to their simplicity and ease of implementation. However, they have several significant limitations. Both converge to a local optimum of their respective objective functions (ignoring the uncertainty in the model space), require the apriori specification of the number of classes/clusters, and are inconsistent. In th...

We give examples of a quantitative analysis of the bivariate Gibbs sampler using coupling arguments. The examples involve standard statistical models – exponential families with conjugate priors or location families with natural priors. Our main approach uses a single eigenfunction (always explicitly available in the examples in question) and stochastic monotonicity.

This paper discusses an iterative multiuser receiver for codedivision multiple access (CDMA) with forward error control coding. The receiver is derived from the maximum aposteriori (MAP) criterion for the joint received signal. A major drawback of the MAP receiver is its heavy computational cost that grows exponentially with the number of users. An alternative solution is proposed here based on...

It is a pleasure to congratulate the authors for this excellent, original and pedagogical paper. I read a preliminary draft at the end of 2006 and I then mentioned to the authors that their work should be set within the framework of Lancaster probabilities, a remoted corner of the theory of probability, now described in their Section 6.1. The reader is referred to Lancaster (1958, 1963, 1975) a...