Adaptive approximate Bayesian computation
About
Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version. While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior. An alternative version based on genuine importance sampling arguments bypasses this difficulty, in connection with the population Monte Carlo method of Cappe et al. (2004), and it includes an automatic scaling of the forward kernel. When applied to a population genetics example, it compares favourably with two other versions of the approximate algorithm.
Mark A. Beaumont, Jean-Marie Cornuet, Jean-Michel Marin, Christian P. Robert• 2008
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Simulation-Based Inference | SBIBM Gaussian Linear | C2ST0.8 | 19 | |
| Simulation-Based Inference | Gaussian Linear | Computation Time (s)0.25 | 8 | |
| Simulation-Based Inference | Gaussian Mixture | Computation Time (s)0.37 | 8 | |
| Simulation-Based Inference | Bernoulli GLM | Computation Time (s)4.88 | 8 | |
| Simulation-Based Inference | Two Moons | Computation Time (s)0.4 | 8 | |
| Simulation-Based Inference | SLCP | Inference Time (s)6.5 | 8 | |
| Posterior Sampling | SLCP SBI benchmark | C2ST98 | 7 | |
| Posterior Sampling | Bernoulli GLM SBI | C2ST92 | 7 | |
| Posterior Sampling | Gaussian Mixture SBI benchmark | C2ST80 | 7 | |
| Posterior Sampling | Two Moons SBI benchmark | C2ST70 | 6 |
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