Hello,
I have been looking for a Julia package to perform amortized Bayesian inference on simulation-based models. In many areas of science, there are models with unknown/intractable likelihood functions. Unfortunately, Bayesian inference with these models have historically been difficult. Somewhat recently there has been progress in this area using a special type of normalizing flow. This method uses a neural network to optimize summary statistics for approximate Bayesian computation. The end result is very accurate amortized inference which can be used for any model, including those without a known likelihood function. Currently, this method is only implemented in a Python package called BayesFlow.
Given Turing's interest in machine learning and Bayesian inference, I was wondering whether there is interest in adding this method to the Turing ecosystem. I think it would add a lot of value to the community.
Hello,
I have been looking for a Julia package to perform amortized Bayesian inference on simulation-based models. In many areas of science, there are models with unknown/intractable likelihood functions. Unfortunately, Bayesian inference with these models have historically been difficult. Somewhat recently there has been progress in this area using a special type of normalizing flow. This method uses a neural network to optimize summary statistics for approximate Bayesian computation. The end result is very accurate amortized inference which can be used for any model, including those without a known likelihood function. Currently, this method is only implemented in a Python package called BayesFlow.
Given Turing's interest in machine learning and Bayesian inference, I was wondering whether there is interest in adding this method to the Turing ecosystem. I think it would add a lot of value to the community.