Posterior sampling in the Age of Emulators
We investigate posterior sampling strategies for cosmological parameter inference using fully differentiable neural-network likelihood emulators, which provide both rapid likelihood evaluations and automatic differentiation. We compare Metropolis--Hastings (MH), the Metropolis-Adjusted Langevin Algorithm (MALA), Hamiltonian Monte Carlo (HMC), the No U-Turn Sampler (NUTS), and Affine Invariant Ensemble Sampling (AIES) using likelihood emulators constructed with the CLiENT framework. The methods are tested on emulators of both the $\Lambda$CDM model and a sterile-neutrino extension. While NUTS generally converges in the fewest samples, its higher computational cost reduces this advantage when performance is measured by wall time. As a result, MALA and even standard MH remain highly competitive. We further find that whitening and covariance adaptation substantially improve sampling efficiency. The TensorFlow implementations developed for this work are released as the BEST (Batched Emulator Sampling with TensorFlow) package, providing a general framework for sampling arbitrary TensorFlow likelihood functions. The package is available through PyPI as 'best-inference' and on GitHub (at https://github.com/AndreasNygaard/best-inference.git).