| Algorithms for return probabilities for stochastic fluid flows NG Bean, MM O'Reilly, PG Taylor Stochastic Models 21 (1), 149-184, 2005 | 98 | 2005 |
| Hitting probabilities and hitting times for stochastic fluid flows NG Bean, MM O’Reilly, PG Taylor Stochastic processes and their applications 115 (9), 1530-1556, 2005 | 91 | 2005 |
| Algorithms for the Laplace–Stieltjes transforms of first return times for stochastic fluid flows NG Bean, MM O’Reilly, PG Taylor Methodology and Computing in Applied Probability 10 (3), 381-408, 2008 | 61 | 2008 |
| Performance measures of a multi-layer Markovian fluid model NG Bean, MM O’Reilly Annals of Operations Research 160, 99-120, 2008 | 45 | 2008 |
| A stochastic two-dimensional fluid model NG Bean, MM O'Reilly Stochastic Models 29 (1), 31-63, 2013 | 42 | 2013 |
| A stochastic fluid flow model of the operation and maintenance of power generation systems NG Bean, MM O'Reilly, JE Sargison IEEE Transactions on Power Systems 25 (3), 1361-1374, 2010 | 40 | 2010 |
| Hitting probabilities and hitting times for stochastic fluid flows: the bounded model NG Bean, M O'reilly, PG Taylor Probability in the Engineering and Informational Sciences 23 (1), 121-147, 2009 | 38 | 2009 |
| A relative density ratio-based framework for detection of land cover changes in MODIS NDVI time series A Anees, J Aryal, MM O’Reilly, TJ Gale IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2015 | 30 | 2015 |
| The stochastic fluid–fluid model: a stochastic fluid model driven by an uncountable-state process, which is a stochastic fluid model itself NG Bean, MM O’Reilly Stochastic Processes and their Applications 124 (5), 1741-1772, 2014 | 25 | 2014 |
| A generic stochastic model for resource availability in fog computing environments SK Battula, MM O'Reilly, S Garg, J Montgomery IEEE Transactions on Parallel and Distributed Systems 32 (4), 960-974, 2020 | 22 | 2020 |
| Analysis of a mechanistic Markov model for gene duplicates evolving under subfunctionalization TL Stark, DA Liberles, BR Holland, MM O’Reilly BMC evolutionary biology 17, 1-16, 2017 | 20 | 2017 |
| Stochastic model for maintenance in continuously deteriorating systems A Samuelson, A Haigh, MM O'Reilly, NG Bean European Journal of Operational Research 259 (3), 1169-1179, 2017 | 18 | 2017 |
| A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data A Anees, J Aryal, MM O’Reilly, TJ Gale, T Wardlaw ISPRS Journal of Photogrammetry and Remote Sensing 122, 167-178, 2016 | 18 | 2016 |
| On mechanistic modeling of gene content evolution: birth-death models and mechanisms of gene birth and gene retention AI Teufel, J Zhao, M O’Reilly, L Liu, DA Liberles Computation 2 (3), 112-130, 2014 | 14 | 2014 |
| A Markovian approach to power generation capacity assessment of floating wave energy converters E Arzaghi, MM Abaei, R Abbassi, M O'Reilly, V Garaniya, I Penesis Renewable Energy 146, 2736-2743, 2020 | 13 | 2020 |
| Multi-stage stochastic fluid models for congestion control MM O’Reilly European Journal of Operational Research 238 (2), 514-526, 2014 | 13 | 2014 |
| On the decision support model for the patient admission scheduling problem with random arrivals and departures: A solution approach AK Abera, MM O’Reilly, M Fackrell, BR Holland, M Heydar Stochastic Models 36 (2), 312-336, 2020 | 11 | 2020 |
| Level-dependent QBD models for the evolution of a family of gene duplicates J Diao, TL Stark, DA Liberles, MM O’Reilly, BR Holland Stochastic Models 36 (2), 285-311, 2020 | 10 | 2020 |
| Decision support model for the patient admission scheduling problem with random arrivals and departures A Abera, M O'Reilly, B Holland, M Fackrell, M Heydar University of Tasmania, 2019 | 10 | 2019 |
| Loss rates for stochastic fluid models MM O’Reilly, Z Palmowski Performance Evaluation 70 (9), 593-606, 2013 | 10 | 2013 |
