best-inference 0.2.1

Batched Emulator Sampling with Tensorflow

BEST

(Batched Emulator Sampling with TensorFlow)

A TensorFlow-based inference framework for high-performance Markov Chain Monte Carlo (MCMC) sampling and profile likelihood optimisation, including support for neural likelihood emulators and adaptive covariance estimation.

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Overview

best provides a unified interface for sampling using multiple MCMC algorithms with GPU acceleration via TensorFlow. It also features an optimisation module for computing profile likelihoods with an arbitrary number of fixed parameters.

Supported mcmc samplers

• Metropolis-Hastings (MH)
• Affine Invariant Ensemble Sampler (AIES)
• Hamiltonian Monte Carlo (HMC)
• No-U-Turn Sampler (NUTS)
• Metropolis Adjusted Langevin Algorithm (MALA)

Key features

• TensorFlow / GPU acceleration
• Automatic covariance matrix adaptation
• Bounded parameter inference
• Parallelised multi-chain sampling
• parallelised optimisation of profile likelihoods
• Pretrained neural likelihood emulators
• JIT compilation (XLA support)

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Installation

From PyPI

pip install best-inference

From source

git clone https://github.com/AndreasNygaard/best-inference.git
cd best-inference
pip install .

---

Quick start

Bayesian MCMC sampling

import best
import tensorflow as tf

def log_prob(x):
    return -0.5 * tf.reduce_sum(x**2, axis=-1)

sampler = best.Sampler(log_prob, bounds=([-5, -5], [5, 5]))

results = sampler.sample(
    method="hmc",
    n_steps=2000,
    n_chains=50,
    initial_distribution="uniform",
    num_burnin_steps=1000
)

print(results.samples.shape)
print(results.loglkl.shape)

Frequentist profile likelihoods

import best
import tensorflow as tf

def log_prob(x):
    return -0.5 * tf.reduce_sum(x**2, axis=-1)

optimiser = best.Optimiser(log_prob, bounds=([-5, -5, -5], [5, 5, 5]))

# 2D profile with the first two parameters fixed (0 and 1)
results = optimiser.compute_profile([0,1])

print(results.full_position.shape)
print(results.loglkl.shape)

---

Sampler API

Initialisation

sampler = best.Sampler(
    log_prob_fn,
    bounds=None,
    enforce_boundaries=True,
    covmat=None,
    initial_state=None,
    n_chains=10,
    initial_distribution="repeat",
    boundary_penalty_factor=10000
)

optimiser = best.Optimiser(
    log_prob_fn,
    bounds,
    covmat=None,
    loc=None,
    mcmc_temperature=1.0
)

Sampling

results_samp = sampler.sample(
    method="mh" | "aies" | "hmc" | "nuts" | "mala",
    n_steps=1000,
    n_chains=10,
    initial_state=None,
    initial_distribution="repeat" | "uniform" | "gaussian",
    bounds=None,
    covmat=None,
    num_burnin_steps=100,
    num_covmat_updates=None,
    update_initial_state=True,
    update_initial_distribution=True,
    continue_distribution=False,
    sampler_kwargs={},
    burnin_kwargs={},
    get_individual_chains=True,
    jit_compile=True,
    temperature=1.0
)

Optimisation

results_opt = optimiser.compute_profile(
    idxs=[], # indices for fixed parameters
    fixed_points=None,
    nbins=20,
    batch_size=10,
    start_temperature=1.0,
    decay_temperature=0.5,
    min_temperature=1e-2,
    step_size=0.05,
    min_step_size=1e-5,
    decay_step_size=0.5,
    max_correct_loglike=10000,
    nd_fixed=None,
    verbose=True
)

Output

results_samp.samples
results_samp.loglkl
results_samp.acceptance_rate
results_samp.evaluations

results_samp.burnin_samples
results_samp.burnin_loglkl
results_samp.burnin_acceptance_rates
results_samp.burnin_evaluations
results_samp.covmat_estimate

results_opt.fixed_points
results_opt.loglkl
results_opt.reduced_position
results_opt.full_position
results_opt.idxs

Client emulators

BEST includes pretrained neural likelihood emulators for cosmology-inspired inference problems.

Available models

• lcdm
• sterile_neutrino

Load a model

from best.client_emulators import load_model_and_scalers

log_prob_fn, lower_bounds, upper_bounds = load_model_and_scalers("lcdm")

Example: emulator-based inference

import best
from best.client_emulators import load_model_and_scalers

log_prob_fn, lower, upper = load_model_and_scalers("lcdm")

sampler = best.Sampler(log_prob_fn, bounds=(lower, upper))

results = sampler.sample(
    method="aies",
    n_steps=5000,
    n_chains=100,
    initial_distribution="uniform",
    num_burnin_steps=2000,
    num_covmat_updates=1
)

Example: emulator-based profile likelihood

import best
from best.client_emulators import load_model_and_scalers

log_prob_fn, lower, upper = load_model_and_scalers("lcdm")

optimiser = best.Optimiser(log_prob_fn, bounds=(lower, upper))

# 2D profile for omega_b and omega_cdm
results = optimiser.compute_profile(
    idxs=[0,1]
)

Supported MCMC algorithms

Metropolis-Hastings (MH)

Random-walk MCMC with optional adaptive covariance scaling.

Affine Invariant Ensemble Sampler (AIES)

Efficient for highly anisotropic or correlated parameter spaces.

Hamiltonian Monte Carlo (HMC)

Gradient-based sampling with leapfrog integration.

No-U-Turn Sampler (NUTS)

Adaptive HMC variant with automatic trajectory length selection.

Metropolis Adjusted Langevin Algorithm (MALA)

Gradient-informed diffusion-based sampler.

Performance notes

• TensorFlow enables GPU acceleration where available
• JIT compilation (XLA) improves performance for large chains
• Vectorized multi-chain execution is used throughout
• Covariance estimation is performed during burn-in when enabled
• Optimiser for profile likelihoods is initialised with an MCMC for exploring the parameter space

Example: Multi-sampler comparison

sampler.set_initial_state(initial_state=means, covmat=covmat, initial_distribution="gaussian")
res_aies = sampler.sample(method="aies", n_steps=5000, n_chains=100)
res_hmc  = sampler.sample(method="hmc",  n_steps=5000, n_chains=100)
res_nuts = sampler.sample(method="nuts", n_steps=5000, n_chains=100)
res_mh   = sampler.sample(method="mh",   n_steps=5000, n_chains=100)
res_mala = sampler.sample(method="mala", n_steps=5000, n_chains=100)

Refining profile likelihoods

The optimiser is initialised by running an MCMC sampler in order to explore the parameter space and estimate the covariance matrix and the best-fit point. The points sampled here allow for an automatic selection of relevant points for the 1D and 2D profile likelihoods (as to not waste computational effort on bad points in a grid).

It can, however, happen that a few points fail to optimise properly, and this can be inspected using the plot_profile_1d and plot_profile_2d methods producing plots like these (with 1-sigma, 2-sigma, and 3-sigma contours shown as well):

results = optimiser.compute_profile([0,1])
optimiser.plot_profile_2d(results)

Here, there are three points that stand out (artificially altered for this example), and these can be recomputed using the methods recompute_points_1d and recompute_points_2d. This will open an interactive version of the plot where points can be selected by clicking them and recomputed using the "Enter" key:

updated_results = optimiser.recompute_points_2d(results)

Even though the automatic point selection worked very well, sometimes a few more points are needed to properly represent the 3-sigma contour well enough. In this case, one can use the methods add_points_1d and add_points_2d. This will also open an interactive version of the plot where new points can be added by clicking the desired position and computed using the "Enter" key:

updated_results = optimiser.recompute_points_2d(updated_results)

When adding or recomputing points for a 2D profile likelihood, the colour scale can be adjusted using the "up" and "down" arrow keys. This can help better compare adjacent points when the span in likelihood values is quite large:

Requirements

• Python ≥ 3.10
• TensorFlow ≥ 2.17
• TensorFlow Probability ≥ 0.24
• NumPy
• tf-keras
• hypersphere-sampler

Citation

If you use this package, please cite:

@article{Nygaard:2026fgl,
    author = "Nygaard, Andreas and Janken, Luca and Hannestad, Steen and Tram, Thomas",
    title = "{Posterior sampling in the Age of Emulators}",
    eprint = "2606.04895",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.IM",
    month = "6",
    year = "2026"
}

Contributing

Contributions are welcome. ###Steps:

• Fork repository
• Create feature branch
• Add tests in tests/
• Submit pull request

License

MIT License

Copyright (c) 2026 Andreas Nygaard