INFERRING PRIMARY EXTINCTION LEVELS IN LATE PERMIAN FOOD WEBS USING APPROXIMATE BAYESIAN COMPUTATION
We then use Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) techniques to solve the inverse problem: namely, inferring the level of perturbation responsible for the Permian extinction, as well as the pattern of extinction among guilds. ABC SMC works by randomly sampling perturbation values from a prior distribution and keeping only those that result in output similar to the observed data. This process is then iterated to sequentially narrow the range of plausible perturbations, thereby arriving at the posterior distribution of perturbation levels. Unlike other methods such as MCMC, ABC SMC does not require calculating the likelihood function, which makes it applicable in a wide range of problems.