TY - JOUR
T1 - On population-based simulation for static inference
AU - Jasra, Ajay
AU - Stephens, David A.
AU - Holmes, Christopher C.
N1 - Generated from Scopus record by KAUST IRTS on 2019-11-20
PY - 2007/9/1
Y1 - 2007/9/1
N2 - In this paper we present a review of population-based simulation for static inference problems. Such methods can be described as generating a collection of random variables {X n} n=1,N in parallel in order to simulate from some target density π (or potentially sequence of target densities). Population-based simulation is important as many challenging sampling problems in applied statistics cannot be dealt with successfully by conventional Markov chain Monte Carlo (MCMC) methods. We summarize population-based MCMC (Geyer, Computing Science and Statistics: The 23rd Symposium on the Interface, pp. 156-163, 1991; Liang and Wong, J. Am. Stat. Assoc. 96, 653-666, 2001) and sequential Monte Carlo samplers (SMC) (Del Moral, Doucet and Jasra, J. Roy. Stat. Soc. Ser. B 68, 411-436, 2006a), providing a comparison of the approaches. We give numerical examples from Bayesian mixture modelling (Richardson and Green, J. Roy. Stat. Soc. Ser. B 59, 731-792, 1997). © 2007 Springer Science+Business Media, LLC.
AB - In this paper we present a review of population-based simulation for static inference problems. Such methods can be described as generating a collection of random variables {X n} n=1,N in parallel in order to simulate from some target density π (or potentially sequence of target densities). Population-based simulation is important as many challenging sampling problems in applied statistics cannot be dealt with successfully by conventional Markov chain Monte Carlo (MCMC) methods. We summarize population-based MCMC (Geyer, Computing Science and Statistics: The 23rd Symposium on the Interface, pp. 156-163, 1991; Liang and Wong, J. Am. Stat. Assoc. 96, 653-666, 2001) and sequential Monte Carlo samplers (SMC) (Del Moral, Doucet and Jasra, J. Roy. Stat. Soc. Ser. B 68, 411-436, 2006a), providing a comparison of the approaches. We give numerical examples from Bayesian mixture modelling (Richardson and Green, J. Roy. Stat. Soc. Ser. B 59, 731-792, 1997). © 2007 Springer Science+Business Media, LLC.
UR - http://link.springer.com/10.1007/s11222-007-9028-9
UR - http://www.scopus.com/inward/record.url?scp=34547863202&partnerID=8YFLogxK
U2 - 10.1007/s11222-007-9028-9
DO - 10.1007/s11222-007-9028-9
M3 - Article
SN - 0960-3174
VL - 17
JO - Statistics and Computing
JF - Statistics and Computing
IS - 3
ER -