Christopher Nemeth

Lecturer in Statistical Learning

Lancaster University

Biography

I am a Lecturer in Statistical Learning in the Department of Mathematics and Statistics and Lancaster University since 2015.

I currently hold an EPSRC fellowship on Scalable and Exact Data Science for Security and Location-based Data and I am a Co-I on the EPSRC-funded Data Science for the Natural Environment.

I work on problems related to Gaussian processes, Bayesian inference and computational statistics, with a focus on Markov chain Monte Carlo and sequential Monte Carlo techniques. My current research is focused on Bayesian methods for Big Data and tools for online learning.

I chair the Royal Statistical Society group on Computational Statistics and Machine Learning RSS-CSML and currently vice-chair of the Statistical Computing section.

Interests

Bayesian Inference
Computational Statistics
Monte Carlo methods
Machine Learning

Education

PhD in Statistics and Operational Research, 2014

Lancaster University
MRes in Statistics and Operational Research, 2011

Lancaster University
BSc in Mathematics, 2006

University of Manchester

Publications

Aicher, C., Putcha, S., Nemeth, C., Fearnhead, P., Fox, E.B.. Stochastic Gradient MCMC for Nonlinear State Space Models. ArXiv (in submission), 2019.

Preprint Code

Fairbrother, J., Nemeth, C., Rischard, M., Brea, J.. GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language. ArXiv (in submission), 2018.

Preprint

Baker, J., Fearnhead, P., Fox, E.B., Nemeth, C.. Control Variates for Stochastic Gradient MCMC. Statistics and Computing (to appear), 2018.

Preprint Code

Baker, J., Fearnhead, P., Fox, E.B., Nemeth, C.. Large-Scale Stochastic Sampling from the Probability Simplex. NeurIPS 2018, 2018.

Preprint PDF Code Poster

Baker, J., Fearnhead, P., Fox, E.B., Nemeth, C.. sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo. Journal of Statistical Software (to appear), 2018.

Preprint Code

Nemeth, C., Sherlock, C.. Merging MCMC Subposteriors through Gaussian-Process Approximations. Bayesian Analysis 13(2):507-530, 2018.

Preprint PDF

Nemeth, C., Lindsten, F., Filippone, M., Hensman, J.. Pseudo-extended Makov chain Monte Carlo. ArXiv (in submission), 2017.

Preprint Code

Nemeth, C., Fearnhead, P., Mihaylova, L.. Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost. Journal of Computational and Graphical Statistics, 25(4):1138-1157, 2016.

Preprint PDF Code

Nemeth, C., Sherlock, C., Fearnhead, P.. Particle Metropolis-adjusted Langevin algorithm. Biometrika, 103(3):701-717, 2016.

Preprint PDF

Nemeth, C.. Parameter Estimation for State Space Models using Sequential Monte Carlo Algorithms. Thesis, 2014.

PDF

Nemeth, C., Fearnhead, P., Mihaylova, L.. Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments. IEEE Transactions on Signal Processing, 62(5):1245-1255, 2014.

PDF Code

Nemeth, C., Fearnhead, P., Mihaylova, L.. Bearings-only tracking with particle filtering for joint parameter learning and state estimation. 15th International Conference on Information Fusion, Singapore, pp.824-831, 2012.

PDF

Nemeth, C., Fearnhead, P., Mihaylova, L.. Particle learning methods for state and parameter estimation. 9th IET Data Fusion and Target Tracking Conference, London, U.K., 2012.

PDF

Software

GaussianProcesses.jl

A nonparametric Bayes package for the Julia language.

Stochastic gradient MCMC

An R package based on Tensorflow for stochastic gradient Monte Carlo sampling.

Teaching

I am currently not teaching, but have previously taught:

2017 - MATH454/554/654: Computationally Intensive Methods
2016 - SCC461: Programming for Data Scientists
2016-2017 - MATH555: Bayesian Inference for Data Scientists
2015-2016 - MATH550: Statistics in Practice

Contact

c.nemeth@lancaster.ac.uk
01524595070
chris.john.nemeth
B81 Postgraduate Statistics Centre, Lancaster University, Lancaster, LA1 4YF, UK