Results 41 to 50 of about 23,712,988 (293)

Discovering transcriptional modules by Bayesian data integration [PDF]

, 2010
Motivation: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data.
Richard S. Savage, Wild, David L., Griffin, JE, Savage, RS, Bernard J. de la Cruz, Savage, Richard S., ,, De la Cruz, Bernard J., David L. Wild, de la Cruz, BJ, Wild, DL, Jim E. Griffin, Ghahramani, Z, Ghahramani, Zoubin, Griffin, Jim E., Zoubin Ghahramani   +15 more
core   +1 more source

Online nonparametric Bayesian analysis of parsimonious Gaussian mixture models and scenes clustering

ETRI Journal, 2020
The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile,
Ri‐Gui Zhou, Wei Wang
doaj   +1 more source

Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures [PDF]

IEEE Transactions on Signal Processing, 2008
Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on Dirichlet process mixtures is introduced.
Francois Caron, Manuel Davy, Arnaud Doucet, Emmanuel Duflos, Philippe Vanheeghe   +4 more
openaire   +6 more sources

Methodology for using a Bayesian nonparametric model to uncover universal patterns in color naming

MethodsX, 2021
Language is an integral part of society which enables communication among its members. To shed light on how words gain their meaning and how their meaning evolves over time, color naming is often used as a case study. The color domain can be defined by a
Kirbi Joe, Maryam Gooyabadi
doaj   +1 more source

Tractography Segmentation Using a Hierarchical Dirichlet Processes Mixture Model [PDF]

NeuroImage, 2009
In this paper, we propose a new nonparametric Bayesian framework to cluster white matter fiber tracts into bundles using a hierarchical Dirichlet processes mixture (HDPM) model. The number of clusters is automatically learned driven by data with a Dirichlet process (DP) prior instead of being manually specified.
Xiaogang Wang 0001, W. Eric L. Grimson, Carl-Fredrik Westin   +2 more
openaire   +6 more sources

Nonparametric Click Modeling Using Dirichlet Process Mixture Model for Information Retrieval

IEEE Access
Click models are essential for comprehending user search behavior and enhancing ranking algorithms; nevertheless, current methodologies face challenges due to the variability of user interaction patterns across different query settings.
K. J. Amala, D. Rajeswari
doaj   +1 more source

Model‐based clustering reveals patterns in central place use of a marine top predator

Ecosphere, 2020
Satellite telemetry data are commonly used to quantify habitat selection, examine animal movements, and delineate home ranges. These data also contain valuable information concerning dens, nests, roosts, and other central places that are often associated
Brian M. Brost, Mevin B. Hooten, Robert J. Small   +2 more
doaj   +1 more source

dpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

IEEE Access, 2020
Comprehensive identification and cataloging of copy number variation (CNVs) are essential to providing a complete view of human genetic variation and to finding diseased genes.
Yaoyao Li, Junying Zhang, Xiguo Yuan, Junping Li   +3 more
doaj   +1 more source

A Predictive Study of Dirichlet Process Mixture Models for Curve Fitting [PDF]

Scandinavian Journal of Statistics, 2013
AbstractThis paper examines the use of Dirichlet process mixtures for curve fitting. An important modelling aspect in this setting is the choice between constant and covariate‐dependent weights. By examining the problem of curve fitting from a predictive perspective, we show the advantages of using covariate‐dependent weights.
S. Wade, S. Walker, PETRONE, SONIA
openaire   +3 more sources

R/BHC : fast Bayesian hierarchical clustering for microarray data [PDF]

, 2009
Background: Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to uncertainty in the results obtained ...
Grant, Murray, Katherine J Denby, Savage, R, Savage, Richard S., Xu, Yang, Denby Katherine J, Denby, Katherine J., David L Wild, Ghahramani, Z, Grant Murray, Richard S Savage, Truman, W, Truman, William M., Grant, M, Wild, David L., Murray Grant, Denby, K, Savage Richard S, William M Truman, Xu, Y, Ghahramani Zoubin, Truman William M, Heller Katherine, Katherine Heller, ,, Wild David L, Heller, KA, Heller, K. (Katherine), Yang Xu, Wild, DL, Ghahramani, Zoubin, Zoubin Ghahramani, Xu Yang   +32 more
core   +13 more sources