Dr Christopher Nemeth

Lecturer in Statistics

Profile

My research is in the areas of computational statistics and statistical machine learning, specifically Markov chain Monte Carlo, sequential Monte Carlo, Gaussian processes and approximate Bayesian computation for intractable likelihoods. Currently, I am working on the problem of efficient Bayesian inference for big data problems via distributed computing and data sub-sampling. My research has an impact in a variety of application areas including target tracking, ecology and econometrics and I am currently collaborating extensively with a number of climate scientists on environmental data science challenges.

Bayesian and Computational Statistics, Statistical Learning, STOR-i Centre for Doctoral Training

STOR-i Centre for Doctoral Training

Bayesian and Computational Statistics, Statistical Learning, STOR-i Centre for Doctoral Training

STOR-i Centre for Doctoral Training

  • DSI - Foundations
  • Statistical Learning
  • STOR-i Centre for Doctoral Training