Seminars

Sampling in high-dimensional spaces presents a considerable challenge in various areas of statistical computation. Traditional methods like Markov Chain Monte…

As we are entering the big data era with technological advances of data collection, high-dimensional and complex data is becoming…

How can we conduct inference on high-dimensional data when classical asymptotic theory fails because noise accumulates faster than at root-n…

In contemporary research, there exists a wide range of theories and techniques that can be utilized to estimate causal effects…

In the realm of classical statistics, the focus has traditionally been on handling observations that can be represented as real…

Abstract Functional data analysis (FDA) almost always involves smoothing discrete observations into curves, because they are never observed in continuous…

Abstract The exploratory and interactive nature of modern data analysis often introduces selection bias and poses challenges to traditional statistical…

Abstract Particle Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be…

Abstract This thesis consists of two separate projects: Total variation approximation of fingerprint distribution, and statistical analysis of centred subgraph…

Abstract Gaussian mixture block models are distributions over graphs that strive to model modern networks: to generate a graph from…