Approximate Likelihood

Approximate likelihood methods offer computationally efficient alternatives to traditional likelihood calculations, enabling statistical inference in complex settings where exact likelihoods are intractable. Current research focuses on applying approximate likelihoods to diverse problems, including parameter estimation in high-dimensional data (e.g., using neural networks for density estimation), initial orbit determination in satellite tracking, and epidemiological modeling (e.g., employing Poisson approximations for stochastic compartmental models). These advancements improve the scalability and speed of statistical inference, impacting fields ranging from astronomy and aerospace engineering to public health and machine learning. The development of accurate and efficient approximate likelihood methods is driving progress in various scientific disciplines.