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Dr Patrick Laub

Dr Patrick Laub

Senior Lecturer
Business School
School of Risk and Actuarial Studies

Patrick Laub is a senior lecturer at the 91色情片 School of Risk and Actuarial Studies. His teaching covers artificial intelligence and machine learning courses, with a focus on risk and insurance applications. He holds a PhD in computational applied probability and degrees in software engineering and mathematics. Patrick's research focuses on computationally challenging problems in actuarial data science, with a focus on natural catastrophe modelling and artificial intelligence.
  • Books | 2022
    Laub PJ; Lee Y; Taimre T, 2022, The Elements of Hawkes Processes, Springer Nature
  • Book Chapters | 2020
    Laub P; El Karoui N; Loisel S; Salhi Y, 2020, 'Quickest detection in practice in presence of seasonality: An illustration with call center data', in Insurance Data Analytics Some Case Studies of Advanced Algorithms and Applications
    Book Chapters | 2019
    Asmussen S; Goffard P-O; Laub P, 2019, 'Orthonormal polynomial expansions and lognormal sum densities', in Risk and Stochastics Ragnar Norberg, Wspc (Europe)
  • Journal articles | 2026
    Avanzi B; Dong E; Laub P; Wong B, 2026, 'Distributional Refinement Network: Distributional Forecasting via Deep Learning', Insurance: Mathematics and Economics,
    Journal articles | 2025
    Laub PJ; Lee Y; Pollett PK; Taimre T, 2025, 'Hawkes Models and Their Applications', Annual Review of Statistics and Its Application, 12, pp. 233 - 258,
    Journal articles | 2024
    Ungolo F; Laub P, 2024, 'An Augmented Variable Dirichlet Process mixture model for the analysis of dependent lifetimes', ASTIN Bulletin, 55, pp. 50 - 75,
    Journal articles | 2022
    Lee Y; Laub PJ; Taimre T; Zhao H; Zhuang J, 2022, 'Exact simulation of extrinsic stress-release processes', Journal of Applied Probability, 59, pp. 105 - 117,
    Journal articles | 2021
    Goffard PO; Laub PJ, 2021, 'Approximate Bayesian Computations to fit and compare insurance loss models', Insurance Mathematics and Economics, 100, pp. 350 - 371,
    Journal articles | 2021
    Li J; Zyphur MJ; Sugihara G; Laub PJ, 2021, 'Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata', Stata Journal, 21, pp. 220 - 258,
    Journal articles | 2020
    Goffard PO; Laub PJ, 2020, 'Orthogonal polynomial expansions to evaluate stop-loss premiums', Journal of Computational and Applied Mathematics, 370,
    Journal articles | 2019
    Asmussen S; Laub PJ; Yang H, 2019, 'Phase-Type models in life insurance: fitting and valuation of equity-linked benefits', Risks, 7,
    Journal articles | 2018
    Andersen LN; Laub PJ; Rojas-Nandayapa L, 2018, 'Efficient Simulation for Dependent Rare Events with Applications to Extremes', Methodology and Computing in Applied Probability, 20, pp. 385 - 409,
    Journal articles | 2018
    Parick L; Robert S; Botev Z; Salomone R; Laub P, 2018, 'Monte Carlo estimation of the density of the sum of dependent random variables', Mathematics and Computers in Simulation, 161, pp. 23 - 31,
    Journal articles | 2017
    Asmussen S; Hashorva E; Laub PJ; Taimre T, 2017, 'Tail asymptotics of light-tailed weibull-like sums', Probability and Mathematical Statistics, 37, pp. 235 - 256,
    Journal articles | 2016
    Laub PJ; Asmussen S; Jensen JL; Rojas-Nandayapa L, 2016, 'Approximating the Laplace transform of the sum of dependent lognormals', Advances in Applied Probability, 48, pp. 203 - 215,
  • Working Papers | 2024
    Avanzi B; Dong T; Laub P; Wong B, 2024, Distributional Refinement Network: Distributional Forecasting via Deep Learning, ,
  • Preprints | 2025
    Jang J; Laub PJ; Siu TK; Zhao H, 2025, Arbitrage-free catastrophe reinsurance valuation for compound dynamic contagion claims,
    Preprints | 2024
    Laub PJ; Lee Y; Pollett PK; Taimre T, 2024, Hawkes Models And Their Applications, ,
    Preprints | 2021
    Lee Y; Laub PJ; Taimre T; Zhao H; Zhuang J, 2021, Exact simulation of extrinsic stress-release processes, ,
    Preprints | 2020
    Goffard P-O; Laub PJ, 2020, Approximate Bayesian Computations to fit and compare insurance loss models, ,
    Preprints | 2020
    Laub PJ; Karoui NE; Loisel S; Salhi Y, 2020, Quickest detection in practice in presence of seasonality: An illustration with call center data, ,
    Preprints | 2018
    Taimre T; Laub PJ, 2018, Rare tail approximation using asymptotics and $L^1$ polar coordinates, ,
    Preprints | 2017
    Asmussen S; Hashorva E; Laub PJ; Taimre T, 2017, Tail asymptotics of light-tailed Weibull-like sums, ,
    Preprints | 2017
    Goffard P-O; Laub PJ, 2017, Orthogonal polynomial expansions to evaluate stop-loss premiums, ,
    Preprints | 2017
    Laub PJ; Salomone R; Botev ZI, 2017, Monte Carlo Estimation of the Density of the Sum of Dependent Random Variables,
    Preprints | 2016
    Andersen LN; Laub PJ; Rojas-Nandayapa L, 2016, Efficient simulation for dependent rare events with applications to extremes, ,
    Preprints | 2016
    Asmussen S; Goffard P-O; Laub PJ, 2016, Orthonormal polynomial expansions and lognormal sum densities, ,
    Preprints | 2015
    Laub PJ; Asmussen S; Jensen JL; Rojas-Nandayapa L, 2015, Approximating the Laplace transform of the sum of dependent lognormals, ,
    Preprints | 2015
    Laub PJ; Taimre T; Pollett PK, 2015, Hawkes Processes, ,
    Theses / Dissertations |
    Laub P, Computational methods for sums of random variables,

Patrick's recent research topics include the Hawkes Processes, Approximate Bayesian Computation, and Empirical Dynamic Modelling. Patrick's joint PhD in computational applied probability was completed between the University of Queensland and Aarhus University. For further information, see .

My Teaching

Since 2022, Patrick developed and taught new courses on artificial intelligence & deep learning and their applications to risk and insurance (ACTL3143 and ACTL5111). He also teaches the course聽Statistical Machine Learning for Risk and Actuarial Applications (ACTL5110).