Considerate Approaches to Achieving Sufficiency for ABC model selection

Details Considerate Approaches to Achieving Sufficiency for ABC model selection Considerate Approaches to Achieving Sufficiency for ABC model selection

2011-06-30

arxiv.org/abs/1106.6281v2

Statistics

Computation

Scientific paper

For nearly any challenging scientific problem evaluation of the likelihood is problematic if not impossible. Approximate Bayesian computation (ABC) allows us to employ the whole Bayesian formalism to problems where we can use simulations from a model, but cannot evaluate the likelihood directly. When summary statistics of real and simulated data are compared --- rather than the data directly --- information is lost, unless the summary statistics are sufficient. Here we employ an information-theoretical framework that can be used to construct (approximately) sufficient statistics by combining different statistics until the loss of information is minimized. Such sufficient sets of statistics are constructed for both parameter estimation and model selection problems. We apply our approach to a range of illustrative and real-world model selection problems.

Affiliated with

Also associated with

No associations

Rating

Considerate Approaches to Achieving Sufficiency for ABC model selection does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Considerate Approaches to Achieving Sufficiency for ABC model selection, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Considerate Approaches to Achieving Sufficiency for ABC model selection will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-35505