Approximate Bayesian ComputationIt is a method based on Bayesian statistics, which is mainly used to estimate the posterior distribution of model parameters.

Likelihood functions are important in model-based statistical inference, but in complex models, the cost of related calculations may be high. Approximate Bayesian calculations are based on simulating the way data is generated to approximate the effect of the likelihood function. This method is mathematically sound, but it inevitably makes assumptions and approximate inferences.

In recent years, approximate Bayesian computational methods have developed rapidly and are mainly used to analyze complex problems arising in biological sciences, such as population genetics, ecology, epidemiology, and systems biology.