I was trying to reproduce the results to find parameters for the Lotka-Volterra equations using halfnormal prior for each of the parameter(example_i_wanted_to_reproduce from the project Module for approximate bayesian computation so that i can understand the problems in the current implementation as it’s mentioned that -
On the other hand, this implementation is quite unstable, meaning that some runs can show reasonably good results and others can present problems with covariance matrix computation or low acceptance rates. Which results in poor parameter estimation. In future work, we would like to include tunning of the number of acceptance/rejection steps that each chain goes through. This might deal efectively with the low acceptance rate issues. Besides, this SMC-ABC implementation cannot sample from transformed PyMC3 variables, as it encounters boundary issues.
I would be very happy to contribute ,I have also explored some of the recent progress in ABC such as -using non linear regression models of the parameters on the summary statistics and then adaptively improving estimation using importance sampling(paper_link
,as mentioned in the paper it lowers the computational burden as well which is one of the current problem.