scholarly journals A nonparametric Bayesian model for system identification based on a super‐Gaussian distribution

2019 ◽  
Vol 102 (7) ◽  
pp. 32-40
Author(s):  
Hiroki Tanji ◽  
Takahiro Murakami ◽  
Hiroyuki Kamata
Keyword(s):  
System Identification ◽  
Gaussian Distribution ◽  
Bayesian Model ◽  
Nonparametric Bayesian

FIR system identification based on a nonparametric Bayesian model using the Indian buffet process

2016 ◽  
Vol 10 (6) ◽  
pp. 1105-1112 ◽  
Author(s):  
Hiroki Tanji ◽  
Ryo Tanaka ◽  
Takahiro Murakami ◽  
Yoshihisa Ishida
Keyword(s):  
System Identification ◽  
Bayesian Model ◽  
Nonparametric Bayesian ◽  
Indian Buffet Process ◽  
Fir System

Estimation of Flat-topped Gaussian distribution with application in system identification

2016 ◽  
Vol 30 (12) ◽  
pp. 726-738 ◽  
Author(s):  
Ruomu Tan ◽  
Biao Huang ◽  
Zukui Li
Keyword(s):  
System Identification ◽  
Gaussian Distribution

scholarly journals TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

2018 ◽  
Author(s):  
Sini Nagpal ◽  
Xiaoran Meng ◽  
Michael P. Epstein ◽  
Lam C. Tsoi ◽  
Matthew Patrick ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Bayesian Model ◽  
Genetic Architecture ◽  
Bayesian Method ◽  
Association Studies ◽  
Gwas Data ◽  
Nonparametric Bayesian ◽  
Transcriptomic Data ◽  
Special Cases

AbstractThe transcriptome-wide association studies (TWAS) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, we propose an improved Bayesian method that assumes a data-driven nonparametric prior to impute gene expression. Our method is general and flexible and includes both the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian method fitted transcriptomic imputation models for 2X number of genes with 1.7X average regression R2 over PrediXcan, thus improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred for modeling the complex genetic architecture of transcriptomes and is expected to enhance transcriptome-integrated genetic association studies. We implement our Bayesian approach in a convenient software tool “TIGAR” (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWAS using individual-level or summary-level GWAS data.

Optimal Nonparametric Bayesian Model-Based Multimodal BoVW Creation Using Multilayer pLSA

2019 ◽  
Vol 39 (2) ◽  
pp. 1123-1132 ◽  
Author(s):  
G. Nagarajan ◽  
R. I. Minu ◽  
A. Jayanthila Devi
Keyword(s):  
Bayesian Model ◽  
Nonparametric Bayesian ◽  
Model Based

scholarly journals A Nonparametric Bayesian Model for Local Clustering With Application to Proteomics

2013 ◽  
Vol 108 (503) ◽  
pp. 775-788 ◽  
Author(s):  
Juhee Lee ◽  
Peter Müller ◽  
Yitan Zhu ◽  
Yuan Ji
Keyword(s):  
Bayesian Model ◽  
Nonparametric Bayesian ◽  
Local Clustering
Sign in / Sign up

Export Citation Format

Share Document