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S Rogers - One of the best experts on this subject based on the ideXlab platform.

  • Variational Bayesian multinomial Probit Regression with gaussian process priors
    NEURAL COMPUT, 2006
    Co-Authors: S Rogers
    Abstract:

    It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over Regression coefficients in favor of gaussian process (GP) priors over functions, and employing variational approximations to the full posterior, we obtain efficient computational methods for GP classification in the multiclass setting.(1) The model augmentation with additional latent variables ensures full a posteriori class coupling while retaining the simple a priori independent GP covariance structure from which sparse approximations, such as multiclass informative vector machines (IVM), emerge in a natural and straightforward manner. This is the first time that a fully variational Bayesian treatment for multiclass GP classification has been developed without having to resort to additional explicit approximations to the nongaussian likelihood term. Empirical comparisons with exact analysis use Markov Chain Monte Carlo (MCMC) and Laplace approximations illustrate the utility of the variational approximation as a computationally economic alternative to full MCMC and it is shown to be more accurate than the Laplace approximation.

  • Variational Bayesian Multinomial Probit Regression with
    2006
    Co-Authors: Mark Girolami, S Rogers
    Abstract:

    It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over Regression coefficients in favor of gaussian process (GP) priors over functions, and employing variational approximations to the full posterior, we obtain efficient computational methods for GP classification in the multiclass setting. 1 The model augmentation with additional latent variables ensures full a posteriori class coupling while retaining the simple a priori independent GP covariance structure from which sparse approximations, such as multiclass informative vector machines (IVM), emerge in a natural and straightforward manner. This is the first time that a fully variational Bayesian treatment for multiclass GP classification has been developed without having to resort to additional explicit approximations to the nongaussian likelihood term. Empirical comparisons with exact analysis use Markov Chain Monte Carlo (MCMC) and Laplace approximations illustrate the utility of the variational approximation as a computationally economic alternative to full MCMC and it is shown to be more accurate than the Laplace approximation.

Jin-guan Lin - One of the best experts on this subject based on the ideXlab platform.

Mark Girolami - One of the best experts on this subject based on the ideXlab platform.

  • AISTATS - Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel
    2014
    Co-Authors: Vassilios Stathopoulos, Kate E. Jones, Veronica Zamora-gutierrez, Mark Girolami
    Abstract:

    We study the problem of identifying bat species from echolocation calls in order to build automated bioacoustic monitoring algorithms. We employ the Dynamic Time Warping algorithm which has been successfully applied for bird ight calls identication and show that classication performance is superior to hand crafted call shape parameters used in previous research. This highlights that generic bioacoustic software with good classication rates can be constructed with little domain knowledge. We conduct a study with eld data of 21 bat species from the north and central Mexico using a multinomial Probit Regression model with Gaussian process prior and a full EP approximation of the posterior of latent function values. Results indicate high classication accuracy across almost all classes while misclassication rate across families of species is low highlighting the common evolutionary path of echolocation in bats.

  • Bat call identification with Gaussian process multinomial Probit Regression and a dynamic time warping kernel
    2014
    Co-Authors: Stathopoulos, Zamora-gutierrez, Kate E. Jones, Mark Girolami
    Abstract:

    We study the problem of identifying bat species from echolocation calls in order to build automated bioacoustic monitoring algorithms. We employ the Dynamic Time Warping algorithm which has been successfully applied for bird flight calls identification and show that classification performance is superior to hand crafted call shape parameters used in previous research. This highlights that generic bioacoustic software with good classification rates can be constructed with little domain knowledge. We conduct a study with field data of 21 bat species from the north and central Mexico using a multinomial Probit Regression model with Gaussian process prior and a full EP approximation of the posterior of latent function values. Results indicate high classification accuracy across almost all classes while misclassification rate across families of species is low highlighting the common evolutionary path of echolocation in bats.

  • vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R
    Bioinformatics (Oxford England), 2007
    Co-Authors: Nicola Lama, Mark Girolami
    Abstract:

    Summary: vbmp is an R package for Gaussian Process classification of data over multiple classes. It features multinomial Probit Regression with Gaussian Process priors and estimates class membership posterior probability employing fast variational approximations to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. Being equipped with only one main function and reasonable default values for optional parameters, vbmp combines flexibility with ease of usage as is demonstrated on a breast cancer micro-array study. Availability: The R library vbmp implementing this method is part of Bioconductor and can be downloaded from http://bioconductor.org/

  • Variational Bayesian Multinomial Probit Regression with
    2006
    Co-Authors: Mark Girolami, S Rogers
    Abstract:

    It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over Regression coefficients in favor of gaussian process (GP) priors over functions, and employing variational approximations to the full posterior, we obtain efficient computational methods for GP classification in the multiclass setting. 1 The model augmentation with additional latent variables ensures full a posteriori class coupling while retaining the simple a priori independent GP covariance structure from which sparse approximations, such as multiclass informative vector machines (IVM), emerge in a natural and straightforward manner. This is the first time that a fully variational Bayesian treatment for multiclass GP classification has been developed without having to resort to additional explicit approximations to the nongaussian likelihood term. Empirical comparisons with exact analysis use Markov Chain Monte Carlo (MCMC) and Laplace approximations illustrate the utility of the variational approximation as a computationally economic alternative to full MCMC and it is shown to be more accurate than the Laplace approximation.

Aijun Yang - One of the best experts on this subject based on the ideXlab platform.

  • Sparse bayesian kernel multinomial Probit Regression model for high-dimensional data classification
    Communications in Statistics - Theory and Methods, 2018
    Co-Authors: Aijun Yang, Xuejun Jiang, Lianjie Shu, Pengfei Liu
    Abstract:

    AbstractIn this paper we introduce a sparse Bayesian kernel multinomial Probit Regression model for multi-class cancer classification. The relationship between the cancer types and gene expression measurements is explained by an unknown function which belongs to an abstract functional space like the reproducing kernel Hilbert space. We assign a sparse prior for Regression parameters and perform variable selection by indexing the covariates of the model with a binary vector. The correlation prior for the binary vector assigned in this paper is able to distinguish models with the same size. The proposed method is successfully tested on one simulated data set and two publicly available real life data sets.

  • Sparse Bayesian multinomial Probit Regression model with correlation prior for high-dimensional data classification
    Statistics & Probability Letters, 2016
    Co-Authors: Aijun Yang, Xuejun Jiang, Pengfei Liu, Jin-guan Lin
    Abstract:

    Selecting a small number of relevant genes for cancer classification has received a great deal of attention in microarray data analysis. In this paper, a sparse Bayesian multinomial Probit Regression model with correlation prior is proposed. Based on simulated and real datasets, we demonstrate that the proposed method performs better than five other competing methods in terms of variable selection and classification.

Catherine A. Calder - One of the best experts on this subject based on the ideXlab platform.

  • Data augmentation strategies for the Bayesian spatial Probit Regression model
    Computational Statistics & Data Analysis, 2012
    Co-Authors: Candace Berrett, Catherine A. Calder
    Abstract:

    The well known latent variable representation of the Bayesian Probit Regression model due to Albert and Chib (1993) allows model fitting to be performed using a simple Gibbs sampler. In addition, various types of dependence among categorical outcomes not explained by covariate information can be accommodated in a straightforward manner as a result of this latent variable representation of the model. One example of this is the spatial Probit Regression model for spatially-referenced categorical outcomes. In this setting, commonly used covariance structures for describing residual spatial dependence in the normal linear model setting can be imbedded into the Probit Regression model. Capturing spatial dependence in this way, however, can negatively impact the performance of MCMC model-fitting algorithms, particularly in terms of mixing and sensitivity to starting values. To address these computational issues, we demonstrate how the non-identifiable spatial variance parameter can be used to create data augmentation MCMC algorithms. We compare the performance of several non-collapsed and partially collapsed data augmentation MCMC algorithms through a simulation study and an analysis of land cover data.