Approximate Bayesian computation can be understood as a kind of Bayesian version of indirect inference. Several efficient Monte Carlo based approaches have ...
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A new field of Bayesian deep learning has emerged that relies on approximate Bayesian inference to provide uncertainty estimates for neural networks.
Often referred to as approximate Bayesian computation (ABC), these methods take summary statistics of simulated data, compare them to observed data.
In this chapter we discuss Approximate Bayesian Computation (ABC). The “approximate” in ABC refers to the lack of explicit likelihood.
Mar 14, 2021 · This simple tutorial demonstrates the power of ABC techniques to provide accurate inference of a parameter's posterior distribution.
Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions.
Approximate Bayesian Computation and Synthetic Likelihoods are two approximate methods for inference, with ABC vastly more popular and with older origins. We ...
The 6th Symposium on Advances in Approximate Bayesian Inference (AABI) will discuss this impact of Bayesian inference, connecting approximate inference methods ...
Nov 17, 2019 · Approximate Bayesian Computation (ABC) is a family of methods that can be used when likelihood is intractable. Approximate Bayesian Inference seems to be used ...
Indeed, approximate Bayesian methods are an increasingly common tool in the arsenal of the practicing statistician and allow users to conduct reliable inference ...