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Open Access research that better understands changing marine ecologies...

Strathprints makes available scholarly Open Access content by researchers in the Department of Mathematics & Statistics.

Mathematics & Statistics hosts the Marine Population Modelling group which is engaged in research into topics surrounding marine resource modelling and ecology. Recent work has included important developments in the population modelling of marine species.

Explore the Open Access research of Mathematics & Statistics. Or explore all of Strathclyde's Open Access research...

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Group by: Publication Date | Item type | No Grouping
Jump to: 2025 | 2024 | 2023 | 2022 | 2021 | 2011
Number of items: 7.

2025

Finkelstein, Maxim and Cha, Ji Hwan (2025) Heterogeneity in populations and the paradoxes of survival : a tribute to Nozer Singpurwalla. Applied Stochastic Models in Business and Industry, 41 (1). ISSN 1524-1904

2024

Finkelstein, Maxim and Cha, Ji Hwan (2024) On a new point process approach to reliability improvement modeling for repairable systems. Applied Stochastic Models in Business and Industry. ISSN 1524-1904

Cha, Ji Hwan and Finkelstein, Maxim (2024) On the multiattempt minimal repair and the corresponding counting process. Applied Stochastic Models in Business and Industry, 40 (1). pp. 206-215. ISSN 1524-1904

2023

Zhang, Mimi and Liu, Bin (2023) Discussion of signature-based models of preventive maintenance. Applied Stochastic Models in Business and Industry, 39 (1). pp. 54-56. ISSN 1524-1904

2022

Cha, Ji Hwan and Finkelstein, Maxim (2022) Discussion of "An overview of some classical models and discussion of the signature-based models of preventive maintenance". Applied Stochastic Models in Business and Industry, 39 (1). pp. 82-84. ISSN 1524-1904

2021

Finkelstein, Maxim and Cha, Ji Hwan and Chakraborty, Niladri (2021) Balancing load and performance for different failure models. Applied Stochastic Models in Business and Industry, 38 (2). pp. 323-333. ISSN 1524-1904

2011

Neslo, REJ and Cooke, RM (2011) Modeling and validating stakeholder preferences with probabilistic inversion. Applied Stochastic Models in Business and Industry, 27 (2). pp. 115-130.

This list was generated on Tue Apr 1 22:21:44 2025 BST.