Working papers

  • Bouranis L., Demiris, N., Barmpounakis, P. and Kalogeropoulos, K., (2026). Exchangeable Gaussian processes with application to epidemics. Preprint.

  • Aschermayr, P. Demiris, N. and Kalogeropoulos, K. (2026). Semi-Markov models with particle-based Bayesian inference for epidemics. Preprint

  • Dubiel-Teleszynski, T., Kalogeropoulos, K. and Karouzakis, N. (2026). Dynamic inference in term structure models with unspanned latent risks. Preprint

  • Vamvourellis, K. Phillips, L. and Kalogeropoulos, K. (2026). Bayesian benefit-risk sssessment with dependent outcomes via latent factor models. Preprint

  • Giuduce, G. Geneletti, S. and Kalogeropoulos, K. (2026). Evaluating the Impact of COVID-19 Vaccination in the UK: A Gaussian Process Approach. Preprint

Journal Articles

  • Bouranis L., Demiris, N., Kalogeropoulos, K. and Ntzoufras, I., (2025). Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19.
    Journal of Royal Statistical Society: Series A.
    Paper | Preprint

  • Chatzilena A., Demiris N. and Kalogeropoulos K. (2024) A modelling framework for the analysis of the transmission of SARS-CoV2.
    Statistics in Medicine 43(23): 2542-2558.
    Paper | Preprint

  • Dubiel-Teleszynski, T., Kalogeropoulos, K. and Karouzakis, N. (2023). Sequential learning and economic benefits from dynamic term structure models.
    Management Science 70(4):2236-2254.
    Paper | Preprint

  • Vamvourellis, K., Kalogeropoulos, K. and Moustaki, I. (2023). Assessment of Generalised Bayesian Structural Equation Models for Continuous and Binary Data.
    British Journal of Mathematical and Statistical Psychology, 76(3), 559–584.
    Paper | Preprint

  • Malesios, C., Demiris, N., Kalogeropoulos, K., and Ntzoufras, I. (2017).
    Bayesian epidemic models for spatially aggregated count data.
    Statistics in Medicine, 36(20), 3216–3230.
    Paper | Preprint

  • Dureau, J., Kalogeropoulos, K., Vickerman, P., Pickles, M., and Boily, M.-C. (2016).
    A Bayesian approach to estimate changes in condom use from limited HIV prevalence data.
    Journal of the Royal Statistical Society: Series C, 65(2), 237–257.
    Paper | Preprint

  • Beskos, A., Dureau, J., and Kalogeropoulos, K. (2015).
    Bayesian inference for partially observed SDEs driven by fractional Brownian motion.
    Biometrika, 102(4), 809–827.
    Paper | Preprint

  • Dureau, J., Kalogeropoulos, K., and Baguelin, M. (2013).
    Capturing the time-varying drivers of an epidemic using stochastic dynamical systems.
    Biostatistics, 14(3), 541–555.
    Paper | Preprint

  • Beskos, A., Kalogeropoulos, K., and Pazos, E. (2013).
    Advanced Markov chain Monte Carlo methods for sampling on diffusion pathspace.
    Stochastic Processes and their Applications, 123(4), 1415–1453.
    Paper | Preprint

  • Kalogeropoulos, K., Dellaportas, P., and Roberts, G. O. (2011).
    Likelihood-based inference for correlated diffusions.
    Canadian Journal of Statistics, 39(1), 52–72.
    Paper | Preprint

  • Kalogeropoulos, K., Roberts, G. O., and Dellaportas, P. (2010).
    Inference for stochastic volatility models using time change transformations.
    Annals of Statistics, 38(2), 784–807.
    Paper | Preprint

  • Kalogeropoulos, K. (2007).
    Likelihood-based inference for a class of multivariate diffusions with unobserved paths.
    Journal of Statistical Planning and Inference, 137, 3092–3102.
    Paper | Preprint

Discussion

  • Kalogeropoulos, K. and Papaspiliopoulos, O. (2008).
    Discussion on Goubar et al. (2008).
    Journal of the Royal Statistical Society: Series A, 171(3).

  • Kalogeropoulos, K. (2006).
    Discussion on Beskos et al. (2006).
    Journal of the Royal Statistical Society: Series B, 68(3).

Conference and Workshop Papers

  • Kalogeropoulos, K., Demiris, N., and Papaspiliopoulos, O. (2008).
    Diffusion-driven models for physiological processes.
    International Workshop on Applied Probability (IWAP).

  • Kalogeropoulos, K., Roberts, G. O., and Dellaportas, P. (2006).
    Irreducible MCMC schemes for diffusion-driven stochastic volatility models.
    Nonlinear Statistical Signal Processing Workshop (NSSPW).