With the availability of high-throughput sequencing data, identification of genetic causal variants accurately requires the efficient incorporation of function annotation data into the optimization routine. This motivates the need for development of novel methods for genome wide association studies with special focus on fine-mapping capabilities. A penalty function method that is simple to impl...

In this paper we consider a Bayesian analysis of contingency tables allowing for the possibility that cells may have probability zero. In this sense we depart from standard log-linear modeling that implicitly assumes a positivity constraint. Our approach leads us to consider mixture models for contingency tables, where the components of the mixture, which we call model-instances, have distinct ...

Mixture of Gaussian processes models extended a single Gaussian process with ability of modeling multi-modal data and reduction of training complexity. Previous inference algorithms for these models are mostly based on Gibbs sampling, which can be very slow, particularly for large-scale data sets. We present a new generative mixture of experts model. Each expert is still a Gaussian process but ...

We propose a non-linear Canonical Correlation Analysis (CCA) method which works by coordinating or aligning mixtures of linear models. In the same way that CCA extends the idea of PCA, our work extends recent methods for non-linear dimensionality reduction to the case where multiple embeddings of the same underlying low dimensional coordinates are observed, each lying on a different high dimens...

We study full Bayesian procedures for sparse linear regression when errors have a symmetric but otherwise unknown distribution. The unknown error distribution is endowed with a symmetrized Dirichlet process mixture of Gaussians. For the prior on regression coefficients, a mixture of point masses at zero and continuous distributions is considered. We study behavior of the posterior with divergin...

We present the log-linear mixture model as a fully discriminative approach to object category recognition which can, analogously to kernelised models, represent non-linear decision boundaries. This model is applied to the problem of recognising object classes in natural images, which is one of the most fundamental and best researched problems in computer vision. Similarly to many recent approac...

this paper considers the search problem, introduced by srivastava cite{sr}. this is a model discrimination problem. in the context of search linear models, discrimination ability of search designs has been studied by several researchers. some criteria have been developed to measure this capability, however, they are restricted in a sense of being able to work for searching only one possible non...

We present a new approach to construct kernels used on support vector machines for speaker verification. The idea is to learn new kernels by taking linear combination of many kernels such as the Generalized Linear Discriminant Sequence kernels (GLDS) and Gaussian Mixture Models (GMM) supervector kernels. In this new linear kernel combination, the weights are speaker dependent rather than univer...

We present a generic framework to track shapes across large variations by learning non-linear shape manifold as overlapping, piecewise linear subspaces. We use landmark based shape analysis to train a Gaussian mixture model over the aligned shapes and learn a Point Distribution Model(PDM) for each of the mixture components. The target shape is searched by first maximizing the mixture probabilit...