I am a Research Fellow working with Theo Damoulas and Adam Johansen on simulations of Gaussian and deep Gaussian processes. Before that I was a research fellow (at Warwick) with Wilfrid Kendall exploring the use of Dirichet forms to investigate theoretical properties (particularly optimal scaling) of MCMC methods. From April 2017 until September 2018 I was a postdoctoral research assistant at Queen Mary University of London with John Moriarty, working on simulation of rare events and other applications of MCMC methods to energy related problems. I did my PhD (2013-2017) at Imperial College London with Alex Mijatović on the topic of variance reduction using control variates obtained by approximately solving the Poisson equation. My main research interest focuses on methodological and theoretic aspects of computational statistical algorithms.
I was co-organising Algorithms & Computationally Intensive Inference seminar between May 19 and Jul 21
- J. Vogrinc, S. Livingstone, & G. Zanella. "Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms."
- M. Goodridge, J. Moriarty, J. Vogrinc, A. Zocca. "Hopping between distant basins."
- J. Vogrinc & W. S. Kendall (2021). "Counterexamples for optimal scaling of Metropolis-Hastings chains with rough target densities." The Annals of Applied Probability
- J. Moriarty, J. Vogrinc & A. Zocca (2021). "The Skipping Sampler: A new approach to sample from complex conditional densities." Statistics and Computing
- A. Mijatović & J. Vogrinc. (2019) "Asymptotic variance for Random Walk Metropolis chains in high dimensions: logarithmic growth via the Poisson equation." Advances in Applied Probability
- A. Mijatović & J. Vogrinc. (2018) "On the Poisson equation for Metropolis-Hastings chains." Bernoulli
Mathematical Sciences Building
Rm MB2.13, Dept of Statistics
University of Warwick
Coventry, CV4 7AL