Sequential Bayesian inference in hidden Markov stochastic kinetic models with application to detection and response to seasonal epidemics-Reference-Cited by-同舟云学术

Sequential Bayesian inference in hidden Markov stochastic kinetic models with application to detection and response to seasonal epidemics

Published:2013-08-22 Issue:6 Volume:24 Page:1047-1062
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Lin Junjing,Ludkovski Michael

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s11222-013-9419-z.pdf

Reference46 articles.

1. Amrein, M., Künsch, H.: Rate estimation in partially observed Markov jump processes with measurement errors. Stat. Comput. 22(2), 513–526 (2012)

2. Lecture Notes in Statistics;H. Andersson,2000

3. Andrieu, C., Doucet, A., Holenstein, R.: Particle Markov chain Monte Carlo methods. J. R. Stat. Soc. B 72(3), 269–342 (2010)

4. Ball, F., Neal, P.: A general model for stochastic SIR epidemics with two levels of mixing. Math. Biosci. 180, 73–102 (2002)

5. Bellomy, A.: Influenza surveillance activities local update report. Technical report, Santa Barbara County Public Health Department (2011) http://www.countyofsb.org/phd/epi.aspx?id=23610&ekmensel=15074a7f_1152_1272_23610_1

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