A non‐parametric approach to the change‐point problem AN Pettitt Journal of the Royal Statistical Society: Series C (Applied Statistics) 28 …, 1979 | 3071 | 1979 |

An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants J Møller, AN Pettitt, R Reeves, KK Berthelsen Biometrika 93 (2), 451-458, 2006 | 425 | 2006 |

Marginal likelihood estimation via power posteriors N Friel, AN Pettitt Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2008 | 350 | 2008 |

Importance nested sampling and the MultiNest algorithm F Feroz, MP Hobson, E Cameron, AN Pettitt arXiv preprint arXiv:1306.2144, 2013 | 319 | 2013 |

A simple cumulative sum type statistic for the change-point problem with zero-one observations AN Pettitt Biometrika 67 (1), 79-84, 1980 | 238 | 1980 |

Overcrowding and understaffing in modern health-care systems: key determinants in meticillin-resistant Staphylococcus aureus transmission A Clements, K Halton, N Graves, A Pettitt, A Morton, D Looke, M Whitby The Lancet infectious diseases 8 (7), 427-434, 2008 | 235 | 2008 |

High incentive effects on vigilance performance during 72 hours of total sleep deprivation JA Horne, AN Pettitt Acta psychologica 58 (2), 123-139, 1985 | 225 | 1985 |

Estimation of parameters for macroparasite population evolution using approximate Bayesian computation CC Drovandi, AN Pettitt Biometrics 67 (1), 225-233, 2011 | 208 | 2011 |

The Kolmogorov-Smirnov goodness-of-fit statistic with discrete and grouped data AN Pettitt, MA Stephens Technometrics 19 (2), 205-210, 1977 | 187 | 1977 |

Proportional odds models for survival data and estimates using ranks AN Pettitt Journal of the Royal Statistical Society: Series C (Applied Statistics) 33 …, 1984 | 182 | 1984 |

Inference for the linear model using a likelihood based on ranks AN Pettitt Journal of the Royal Statistical Society: Series B (Methodological) 44 (2 …, 1982 | 155 | 1982 |

A review of modern computational algorithms for Bayesian optimal design EG Ryan, CC Drovandi, JM McGree, AN Pettitt International Statistical Review 84 (1), 128-154, 2016 | 145 | 2016 |

A stochastic mathematical model of methicillin resistant *Staphylococcus aureus* transmission in an intensive care unit: Predicting the impact of interventionsES McBryde, AN Pettitt, DLS McElwain Journal of theoretical biology 245 (3), 470-481, 2007 | 131 | 2007 |

Modified Cramer-von Mises statistics for censored data AN Pettitt, MA Stephens Biometrika 63 (2), 291-298, 1976 | 131 | 1976 |

Driving performance impairments due to hypovigilance on monotonous roads GS Larue, A Rakotonirainy, AN Pettitt Accident Analysis & Prevention 43 (6), 2037-2046, 2011 | 128 | 2011 |

Sampling designs for estimating spatial variance components AN Pettitt, AB McBratney Journal of the Royal Statistical Society: Series C (Applied Statistics) 42 …, 1993 | 124 | 1993 |

Modeling length of stay in hospital and other right skewed data: comparison of phase‐type, gamma and log‐normal distributions M Faddy, N Graves, A Pettitt Value in Health 12 (2), 309-314, 2009 | 119 | 2009 |

Approximate Bayesian Computation for astronomical model analysis: a case study in galaxy demographics and morphological transformation at high redshift E Cameron, AN Pettitt Monthly Notices of the Royal Astronomical Society 425 (1), 44-65, 2012 | 112 | 2012 |

A conditional autoregressive Gaussian process for irregularly spaced multivariate data with application to modelling large sets of binary data AN Pettitt, IS Weir, AG Hart Statistics and Computing 12 (4), 353-367, 2002 | 109 | 2002 |

Approximate Bayesian computation using indirect inference CC Drovandi, AN Pettitt, MJ Faddy Journal of the Royal Statistical Society: Series C (Applied Statistics) 60 …, 2011 | 102 | 2011 |