The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Asymptotic expansion approximation for spatial structure arising from directionally biased movement
Open accessAbstract
Spatial structure can arise in spatial point process models via a range of mechanisms, including neighbour-dependent directionally biased movement. This spatial structure is neglected by mean-field models, but can have important effects on population dynamics. Spatial moment dynamics are one way to obtain a deterministic approximation of a dynamic spatial point process that retains some information about spatial structure. However, the applicability of this approach is limited by the computational cost of numerically solving spatial moment dynamic equations at a sufficient resolution. We present an asymptotic expansion for the equilibrium solution to the spatial moment dynamics equations in the presence of neighbour-dependent directional bias. We show that the asymptotic expansion provides a highly efficient scheme for obtaining approximate equilibrium solutions to the spatial moment dynamics equations when bias is weak. This scheme will be particularly useful for performing parameter inference on spatial moment models.
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“This approach has been used in generalization and in classification tasks [80, 156], with Refs. [76, 80, 160, 161] discussing different ways of embedding classical data into quantum states (such as data re-uploading), and with Ref. [158] showing an experimental demonstration of variational classification.”
From Variational quantum algorithms (2021) · cites this paper as [160]
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Cites
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Kirkwood, J. G. (1935) · The Journal of Chemical Physics · doi:10.1063/1.1749657 · Cited by 3,454
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Sequential Monte Carlo without likelihoods
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POPULATION GROWTH IN SPACE AND TIME: SPATIAL LOGISTIC EQUATIONS
Law, R., Murrell, D. J., & Dieckmann, U. (2003) · Ecology · doi:10.1890/0012-9658(2003)084[0252:pgisat]2.0.co;2 · Cited by 366
Neighborhood Models of Plant Population Dynamics. I. Single-Species Models of Annuals
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Heteromyopia and the spatial coexistence of similar competitors
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Modeling and analysis of stochastic invasion processes
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Cited by
Variational quantum algorithms
Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., McClean, J. R., Mitarai, K., Yuan, X., Cincio, L., et al. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00348-9 · Cited by 3,385
Metadata
- Authors
- Plank, M. J.
- Affiliations
- University of Canterbury (NZ)
- Corresponding author
- Plank, Michael J.
- ORCID
- Plank, Michael J.
- Venue
- Physica A: Statistical Mechanics and its Applications
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 541
- Pages
- 123290
- ISSN
- 0378-4371 (ISSN-L)
- OpenAlex
- W2981920486
- Cited by
- 1
- References
- 33
- Citations by year
- 2021: 1
- FWCI
- 0.23
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Locations
- Physica A Statistical Mechanics and its Applications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UC Research Repository (University of Canterbury) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Te Pūnaha Matatini, New Zealand
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Physical Sciences › Mathematics › Mathematical Physics › Stochastic processes and statistical mechanics
- Physical Sciences › Mathematics › Applied Mathematics › Point processes and geometric inequalities
- Keywords
- spatial point processes, asymptotic expansion, spatial structure, equilibrium solutions, parameter inference, population dynamics
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- Plank, M. J. (2020). Asymptotic expansion approximation for spatial structure arising from directionally biased movement. Physica A: Statistical Mechanics and its Applications. https://doi.org/10.1016/j.physa.2019.123290
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
