7 papers
Particle Gibbs Sampling for Bayesian Phylogenetic inference
Shijia Wang, Liangliang Wang
The combinatorial sequential Monte Carlo (CSMC) has been demonstrated to be an efficient complementary method to the standard Markov chain Monte Carlo (MCMC) for Bayesian phylogene…
Random Tessellation Forests
Shufei Ge, Shijia Wang, Yee Whye Teh +2
Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursi…
Spectral Dynamic Causal Modelling of Resting-State fMRI: Relating Effective Brain Connectivity in the Default Mode Network to Genetics
Yunlong Nie, Eugene Opoku, Laila Yasmin +8
We conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer's dise…
A Bayesian Spatial Model for Imaging Genetics
Yin Song, Shufei Ge, Jiguo Cao +2
We develop a Bayesian bivariate spatial model for multivariate regression analysis applicable to studies examining the influence of genetic variation on brain structure. Our model…
Recovering the Underlying Trajectory from Sparse and Irregular Longitudinal Data
Yunlong Nie, Yuping Yang, JIguo Cao
In this article, we consider the problem of recovering the underlying trajectory when the longitudinal data are sparsely and irregularly observed and noise-contaminated. Such data…
An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics
Liangliang Wang, Shijia Wang, Alexandre Bouchard-Côté
We describe an "embarrassingly parallel" method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo, based on recent advances in the Sequential Monte Carlo literat…