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2008

Adaptive approximate Bayesian computation

Beaumont, Mark A., Cornuet, Jean-Marie, Marin, Jean-Michel et al.

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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.

Adaptive approximate Bayesian computation · Around

Built on

Population growth of human Y chromosomes: a study of Y chromosome microsatellites
Pritchard, J. K · 1999
Earlier work this paper cites.
Monte Carlo Strategies in Scientific Computing
Liu, J · 2001
Earlier work this paper cites.
Markov chain Monte Carlo without likelihoods
Marjoram, P · 2003
Earlier work this paper cites.
Population Monte Carlo
Cappé, O · 2004
Earlier work this paper cites.

Similar

Sequential Monte Carlo samplers
Del Moral, P · 2006
Cited alongside, same era.
Inference for stereological extremes
Bortot, P · 2007
Cited alongside, same era.
Convergence of adaptive mixtures of importance sampling schemes
Douc, R · 2007
Cited alongside, same era.
Sequential Monte Carlo without likelihoods
Sisson, S. A · 2007
Cited alongside, same era.

Then

Adaptive importance sampling in general mixture classes
Cappé, O · 2008
Closest in time.
Inferring population history with DIYABC: a user-friendly approach to Approximate Bayesian Computation
Cornuet, J.-M · 2008
Closest in time.
Approximate Bayesian computation in population genetics
Beaumont, M · 2035
Closest in time.

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