68 citations · 118 across the 12 of their papers we have counts for
13 papers
Particle Data Cloning for Complex Ordinary Differential Equations
Donghui Son, Liangliang Wang
Ordinary differential equations (ODEs) are fundamental tools for modeling complex dynamic systems across scientific disciplines. However, parameter estimation in ODE models is chal…
Robust Bayesian Functional Principal Component Analysis
Jiarui Zhang, Jiguo Cao, Liangliang Wang
We develop a robust Bayesian functional principal component analysis (RB-FPCA) method that utilizes the skew elliptical class of distributions to model functional data, which are o…
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…
Adaptive semiparametric Bayesian differential equations via sequential Monte Carlo
Shijia Wang, Shufei Ge, Renny Doig +1
Nonlinear differential equations (DEs) are used in a wide range of scientific problems to model complex dynamic systems. The differential equations often contain unknown parameters…
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…
Normal and pathological dynamics of platelets in humans
Gabriel P. Langlois, Morgan Craig, Antony R. Humphries +6
We develop a comprehensive mathematical model of platelet, megakaryocyte, and thrombopoietin dynamics in humans. We show that there is a single stationary solution that can undergo…
Functional Mapping of Multiple Dynamic Traits
Jiguo Cao, Liangliang Wang, Zhongwen Huang +2
Many biological phenomena undergo developmental changes in time and space. Functional mapping, which is aimed at mapping genes that affect developmental patterns, is instrumental f…
Estimating Functional Linear Mixed-Effects Regression Models
Baisen Liu, Jiguo Cao
The functional linear model is a popular tool to investigate the relationship between a scalar/functional response variable and a scalar/functional covariate. We generalize this mo…
A Smooth and Locally Sparse Estimator for Functional Linear Regression via Functional SCAD Penalty
Zhenhua Lin, Jiguo Cao, Liangliang Wang +1
In this paper, we propose a new regularization technique called "functional SCAD". We then combine this technique with the smoothing spline method to develop a smooth and locally s…