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Approximate Bayesian Computation: A Nonparametric Perspective
| Content Provider | Semantic Scholar |
|---|---|
| Author | Blum, Michael |
| Copyright Year | 2009 |
| Abstract | Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well suited to models defined in terms of a stochastic generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds by computing summary statistics sobs from the data and simulating summary statistics for different values of the parameter Θ. The posterior distribution is then approximated by an estimator of the conditional density g(Θ|sobs). In this paper, we derive the asymptotic bias and variance of the standard estimators of the posterior distribution which are based on rejection sampling and linear adjustment. Additionally, we introduce an original estimator of the posterior distribution based on quadratic adjustment and we show that its bias contains a fewer number of terms than the estimator with linear adjustment. Although we find that the estimators with adjustment are not universally superior to the estimator based on rejection sampling, we find that they can achieve better performance ... |
| Starting Page | 1178 |
| Ending Page | 1187 |
| Page Count | 10 |
| File Format | PDF HTM / HTML |
| DOI | 10.1198/jasa.2010.tm09448 |
| Volume Number | 105 |
| Alternate Webpage(s) | https://hal.archives-ouvertes.fr/hal-00373301/document |
| Alternate Webpage(s) | http://arxiv.org/pdf/0904.0635v4.pdf |
| Alternate Webpage(s) | https://arxiv.org/pdf/0904.0635v6.pdf |
| Alternate Webpage(s) | http://membres-timc.imag.fr/Michael.Blum/publications/jasablum2010.pdf |
| Alternate Webpage(s) | https://doi.org/10.1198/jasa.2010.tm09448 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
