What is ABC? In the world of statistics, it stands for Approximate Bayesian Computation. ACEMS' research assistant Abhishek Vargese recently completed an AMSI Vacation Research Experience Scheme (VRES) and wrote this blog about what he learned about ABC:
Complex models are often hard to express as a formula, making it impossible to use classical statistical methods to accurately calibrate the model.
Enter approximate Bayesian computation (ABC).
Also known as simulator-based inference, ABC is a class of statistical methods that test a wide range of possible model parameters and compare the resulting output to the observed data. ABC identifies model parameters that consistently provide model results that match the real dataset closely. Thus, ABC enables relatively accurate calibration in scenarios where it was not possible before!
ABC has been used in a variety of applications. It is widely used for applications in biological sciences e.g., in population genetics, ecology, epidemiology, and systems biology. Recently, ABC has been essential in calibrating models to forecast and predict COVID-19 outbreaks in countries, or to describe the spread of diseases in a plantation. ABC has helped evaluate energy policy, by enabling a complex Agent-Based Model to accurately forecast the fiscal effects of different feed-in tariff structures in Great Britain. Although airline travel is still limited today, ABC has enabled us to use a complex queuing system to accurately model the foot traffic flow in airports.
These are just a few examples where ABC has proven to be invaluable in calibrating models to aid in understanding and solving large problems. The idea behind ABC is intuitive, and effectively leverages the rapid increase in computational power and the availability of large datasets to model processes previously beyond reach. Among many other instruments in a statistician’s toolkit, ABC can help us stand tall when faced with the Goliath-like issues we are surrounded by today and tackle them with greater precision!
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