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Large deviations for the stochastic predator–prey model with nonlinear functional response

Published online by Cambridge University Press:  22 June 2017

M. Suvinthra*
Affiliation:
Bharathiar University
K. Balachandran*
Affiliation:
Bharathiar University
*
* Postal address: Department of Mathematics, Bharathiar University, Coimbatore 641046, India.
* Postal address: Department of Mathematics, Bharathiar University, Coimbatore 641046, India.
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Abstract

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In this paper we consider a diffusive stochastic predator–prey model with a nonlinear functional response and the randomness is assumed to be of Gaussian nature. A large deviation principle is established for solution processes of the considered model by implementing the weak convergence technique.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 2017 

References

[1] Adams, R. A. and Fournier, J. J. F. (2003). Sobolev Spaces, 2nd edn. Elsevier/Academic Press, Amsterdam. Google Scholar
[2] Arratia, R. and Gordon, L. (1989). Tutorial on large deviations for the binomial distribution. Bull. Math. Biol. 51, 125131. CrossRefGoogle ScholarPubMed
[3] Bertini, L. et al. (2007). Large deviations of the empirical current in interacting particle systems. Theory Prob. Appl. 51, 227. CrossRefGoogle Scholar
[4] Bessam, D. (2015). Large deviations in a Gaussian setting: the role of the Cameron-Martin space. In From Particle Systems to Partial Differential Equations II (Springer Proc. Math. Statist. 129), eds P. Goncalves and A. J. Soares, Springer, pp. 135152. CrossRefGoogle Scholar
[5] Bressloff, P. C. and Newby, J. M. (2014). Path integrals and large deviations in stochastic hybrid systems. Phys. Rev. E 89, 042701. CrossRefGoogle ScholarPubMed
[6] Budhiraja, A. and Dupuis, P. (2000). A variational representation for positive functionals of infinite dimensional Brownian motion. Prob. Math. Statist. 20, 3961. Google Scholar
[7] Chen, L. and Jungel, A. (2004). Analysis of a multidimensional parabolic population model with strong cross-diffusion. SIAM J. Math. Anal. 36, 301322. CrossRefGoogle Scholar
[8] Chow, P.-L. (2011). Explosive solutions of stochastic reaction-diffusion equations in mean L p -norm. J. Differential Equat. 250, 25672580. CrossRefGoogle Scholar
[9] Da Prato, G. and Zabczyk, J. (1992). Stochastic Equations in Infinite Dimensions. Cambridge University Press. CrossRefGoogle Scholar
[10] Dembo, A. and Zeitouni, O. (2010). Large Deviations Techniques and Applications. Springer, New York. CrossRefGoogle Scholar
[11] Dupuis, P. and Ellis, R. S. (1997). A Weak Convergence Approach to the Theory of Large Deviations. John Wiley, New York. CrossRefGoogle Scholar
[12] Florens-Landais, D. and Pham, C. H. (1999). Large deviations in estimation of an Ornstein-Uhlenbeck model. J. Appl. Prob. 36, 6077. CrossRefGoogle Scholar
[13] Freidlin, M. (1991). Coupled reaction-diffusion equations. Ann. Prob. 19, 2957. CrossRefGoogle Scholar
[14] Gross, L. (1965). Potential theory on Hilbert space. J. Funct. Anal. 1, 123181. CrossRefGoogle Scholar
[15] Khaminskii, R. Z., Klebaner, F. C. and Liptser, R. (2003). Some results on the Lotka-Volterra model and its small random perturbations. Acta Appl. Math. 78, 201206. CrossRefGoogle Scholar
[16] Klebaner, F. C., Lim, A. and Liptser, R. (2007). FCLT and MDP for stochastic Lotka-Volterra model. Acta Appl. Math. 97, 5368. CrossRefGoogle Scholar
[17] Klebaner, F. C. and Liptser, R. (2001). Asymptotic analysis and extinction in a stochastic Lotka-Volterra model. Ann. Appl. Prob. 11, 12631291. CrossRefGoogle Scholar
[18] Kratz, P., Pardoux, E. and Kepgnou, B. S. (2015). Numerical methods in the context of compartmental models in epidemiology. ESAIM Proc. Surveys 48, 169189. CrossRefGoogle Scholar
[19] Li, A.-W. (2011). Impact of noise on pattern formation in a predator–prey model. Nonlinear Dynamics 66, 689694. CrossRefGoogle Scholar
[20] Manna, U. and Mohan, M. T. (2013). Large deviations for the shell model of turbulence perturbed by Lévy noise. Commun. Stoch. Anal. 7, 3963. Google Scholar
[21] Mo, C. and Luo, J. (2013). Large deviations for stochastic differential delay equations. Nonlinear Anal. 80, 202210. CrossRefGoogle Scholar
[22] Pakdaman, K., Thieuller, M. and Wainrib, G. (2010). Diffusion approximation of birth-death processes: comparison in terms of large deviations and exit points. Statist. Prob. Lett. 80, 11211127. CrossRefGoogle Scholar
[23] Peszat, S. and Zabczyk, J. (2007). Stochastic Partial Differential Equations with Lévy Noise. Cambridge University Press. CrossRefGoogle Scholar
[24] Sambath, M. and Balachandran, K. (2013). Spatiotemporal dynamics of a predator-prey model incorporating a prey refuge. J. Appl. Anal. Comput. 3, 7180. Google Scholar
[25] Sambath, M., Balachandran, K. and Suvinthra, M. (2016). Stability and Hopf bifurcation of a diffusive predator-prey model with hyperbolic mortality. Complexity 21, 3443. CrossRefGoogle Scholar
[26] Setayeshgar, L. (2014). Large deviations for a stochastic Burgers' equation. Commun. Stoch. Anal. 8, 141154. Google Scholar
[27] Shangerganesh, L. and Balachandran, K. (2011). Existence and uniqueness of solutions of predator-prey type model with mixed boundary conditions. Acta Appl. Math. 116, 7186. CrossRefGoogle Scholar
[28] Sivakumar, M., Sambath, M. and Balachandran, K. (2015). Stability and Hopf bifurcation analysis of a diffusive predator-prey model with Smith growth. Internat. J. Biomath. 8, 18 pp. CrossRefGoogle Scholar
[29] Sritharan, S. S. and Sundar, P. (2006). Large deviations for two-dimensional Navier-Stokes equations with multiplicative noise. Stoch. Process. Appl. 116, 16361659. CrossRefGoogle Scholar
[30] Suvinthra, M., Sritharan, S. S. and Balachandran, K. (2015). Large deviations for stochastic tidal dynamics equation. Commun. Stoch. Anal. 9, 477502. Google Scholar
[31] Varadhan, S. R. S. (2008). Large deviations. Ann. Prob. 36, 397419. CrossRefGoogle Scholar
[32] Weber, J. K., Jack, R. L., Schwantes, C. R. and Pande, V. S. (2014). Dynamical phase transitions reveal amyloid-like states on protein folding landscapes. Biophys. J. 107, 974982. CrossRefGoogle ScholarPubMed