Fitting stochastic epidemic models to gene genealogies using linear noise approximation-Reference-Cited by-同舟云学术

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Fitting stochastic epidemic models to gene genealogies using linear noise approximation

Published:2023-03-01 Issue:1 Volume:17 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Tang Mingwei¹,Dudas Gytis²,Bedford Trevor³,Minin Vladimir N.⁴

Affiliation:

1. Department of Statistics, University of Washington, Seattle

2. Gothenburg Global Biodiversity Centre (GGBC)

3. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center

4. Department of Statistics, University of California, Irvine

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference63 articles.

1. Kurtz, T. G. (1970). Solutions of ordinary differential equations as limits of pure jump Markov processes. J. Appl. Probab. 7 49–58.

2. Kurtz, T. G. (1971). Limit theorems for sequences of jump Markov processes approximating ordinary differential processes. J. Appl. Probab. 8 344–356.

3. FEARNHEAD, P., GIAGOS, V. and SHERLOCK, C. (2014). Inference for reaction networks using the linear noise approximation. Biometrics 70 457–466.

4. PAPASPILIOPOULOS, O., ROBERTS, G. O. and SKÖLD, M. (2007). A general framework for the parametrization of hierarchical models. Statist. Sci. 22 59–73.

5. PALACIOS, J. A. and MININ, V. N. (2013). Gaussian process-based Bayesian nonparametric inference of population size trajectories from gene genealogies. Biometrics 69 8–18.

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