An adaptive sequential Monte Carlo method for approximate Bayesian computation

By Pierre Del Moral, Arnaud Doucet, Ajay Jasra. 2011

539 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

K2-ABC: Approximate Bayesian Computation with Infinite Dimensional Summary Statistics via Kernel Embeddings (2015, methodology): “…samples; (2) adaptive algorithms to reduce computational costs such as SMC-ABC that adaptively updates the prior where the parameter samples are drawn(Del Moral et al., 2012) and GPS-ABC that addes a memory in the MCMC algorithm to save useful samples and re-use them (Meeds & Welling, 2014).”
Toward diagnostic model calibration and evaluation: Approximate Bayesian computation (2013, methodology): “This class of methods is also referred to as ABC and is currently a ‘‘hot’’ topic in statistics [Marjoram et al., 2003; Sisson et al., 2007; Del Moral et al., 2011; Joyce and Marjoram, 2008; Grelaud et al., 2009; Ratmann et al., 2009].”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, methodology): “This method is also referred to as approximate Bayesian computation (ABC) (Marjoram et al., 2003; Sis-son et al., 2007; Del Moral et al., 2008; Joyce and Marjo-ram , 2008; Grelaud et et al., 2009; Ratmann et al., 2009) and widens the class of models for which statistical inference can be performed.”
Approximate Bayesian Computation (2013, methodology): “In addition, with sequential methods the tolerance levels must not be specified prior to the analysis, but are adjusted adaptively [46].”
Error Bounds and Normalising Constants for Sequential Monte Carlo Samplers in High Dimensions (2014, unknown): “[14].”
On the stability of sequential Monte Carlo methods in high dimensions (2011, background): “, [18]), approximate Bayesian computation [23] or when one wants to use consistent adaptive algorithms with little user input [32].”
On the Asymptotic Efficiency of Approximate Bayesian Computation Estimators (2015, methodology): “crease the acceptance probability. Alternatives to the importance sampling include MCMC [31,43,41] and sequential Monte Carlo which attempts to move the sample towards the high posterior density area [3,15]. The choice of the proposal distribution is key to the performance of the importance sampling”
Sequential Monte Carlo with Highly Informative Observations (2014, background): “[6], Del Moral, Doucet, and Jasra [13], and Peters, Fan, and Sisson [37].”
Statistical inference for dynamical systems: A review (2012, unknown): “A partial list of references include [23, 25, 80, 96, 101, 103, 111, 119, 125].”