Likelihood Free

Likelihood-free inference tackles the challenge of estimating parameters in complex models where the probability of observing data given parameters (the likelihood) is computationally intractable. Current research focuses on improving the efficiency and accuracy of likelihood-free methods, employing neural networks—such as autoencoders, normalizing flows, and neural posterior estimators—to approximate posterior distributions. These advancements are significantly impacting fields like inverse problems and Bayesian inference by enabling parameter estimation in previously inaccessible models, leading to more robust and efficient analyses of complex systems. Ongoing efforts aim to optimize these neural architectures and integrate them with traditional statistical methods like Approximate Bayesian Computation to enhance both speed and accuracy.