School of Mathematics, Statistics & Actuarial Science


Cristiano completed his PhD studies at SMSAS in 2013, joining the School as a Lecturer in Statistics in 2014.

Contact Information


Room 350

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Also view these in the Kent Academic Repository

Leisen, F., Hinoveanu, L. and Villa, C. (2019). Bayesian Loss-based Approach to Change Point Analysis. Computational Statistics and Data Analysis [Online] 129:61-78. Available at:
Leisen, F., Rossini, L. and Villa, C. (2018). Objective Bayesian Analysis of the Yule-Simon Distribution with Applications. Computational Statistics [Online] 33:99-126. Available at:
Leisen, F., Villa, C. and Marin, J. (2017). Objective Bayesian modelling of insurance risks with the skewed Student-t distribution. Applied Stochastic Models in Business and Industry [Online] 33:136-151. Available at:
Leisen, F., Villa, C. and Rossini, L. (2017). A note on the posterior inference for the Yule-Simon distribution. Journal of Statistical Computation and Simulation [Online] 87:1179-1188. Available at:
Villa, C. (2016). Bayesian estimation of the threshold of a generalised pareto distribution for heavy-tailed observations. Test [Online] 26:95-118. Available at:
Villa, C. and Walker, S. (2015). An Objective Approach to Prior Mass Functions for Discrete Parameter Spaces. Journal of the American Statistical Association [Online] 110:1072-1082. Available at:
Villa, C. (2015). An Objective Bayesian Criterion to Determine Model Prior Probabilities. Scandinavian Journal of Statistics 42:947-966.
Villa, C. (2014). A cautionary note on using the scale prior for the parameter N of a binomial distribution. Ecology 95:2674-2677.
Villa, C. and Walker, S. (2014). Objective prior for the number of degrees of freedom of a t distribution. Bayesian Analysis [Online] 9:197-220. Available at:
Total publications in KAR: 9 [See all in KAR]
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Research Interests

  • Objective Bayesian analysis
  • Bayesian model selection and change point analysis
  • Bayesian modelling for 'big data' problems and cyber-security
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MA319: Probability and Statistics for Actuarial Science
MA529: Probability and Statistics for Actuarial Science 2
MA538: Applied Bayesian Modelling
MA882: Advanced Regression Modelling
MA5507: Mathematical Statistics back to top

Research Supervisees

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School of Mathematics, Statistics and Actuarial Science (SMSAS), Sibson Building, Parkwood Road, Canterbury, CT2 7FS

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Last Updated: 07/09/2018