Approximate Bayesian Computation

Approximate Bayesian Computation (ABC) is a method used in statistics to estimate the likelihood of different hypotheses when traditional calculations are difficult. Instead of directly computing probabilities, ABC involves generating simulated data based on various possible parameters, then comparing these simulations to the actual observed data. If the simulated data closely matches the real data, the corresponding parameters are considered more probable. By repeating this process many times, ABC helps researchers infer the most likely explanations for complex data without needing exact models, making it a valuable tool for understanding uncertain or complex systems.