Wentao Li

Wentao Li is a Lecturer in Statistics at The University of Manchester, Department of Mathematics. His research focuses on Bayesian computation methods, simulation-based inference, and state-space models, with applications to financial time series data. Li's work includes developing new statistical algorithms for complex models, such as stochastic volatility models and Hawkes processes. He is currently accepting PhD students for a project on Bayesian simulation-based inference for time series models. Prior to joining The University of Manchester, Li held positions at the University of Hong Kong, Newcastle University, and Lancaster University. He completed his PhD in Statistics at Rutgers University under the supervision of Rong Chen and Zhiqiang Tan.

Wentao Li is a researcher at The University of Manchester with expertise in Monte Carlo methods, Bayesian statistics, asymptotic theory, and approximate Bayesian computation. His work focuses on developing and analyzing statistical methods for parameter estimation and inference, particularly in complex models such as state-space models. Li's research includes the convergence properties of approximate Bayesian computation (ABC) methods, the efficiency of ABC estimators, and the application of sequential Monte Carlo techniques. His publications explore the theoretical foundations and practical implementations of these methods, contributing to the advancement of computational statistics and Bayesian inference.