MCMC

class nnero.mcmc.Likelihood(parameters: list[str])

Bases: ABC

get_x(theta: ndarray, xi: ndarray) → ndarray

get_x_dict(x: ndarray) → dict

get_x_dicts(x: ndarray) → list[dict]

loglkl(theta: ndarray, xi: ndarray, **kwargs) → ndarray

class nnero.mcmc.OpticalDepthLikelihood(parameters: list[str], *, classifier: Classifier, regressor: Regressor, median_tau: float = 0.0544, sigma_tau: float = 0.0073)

Bases: Likelihood

class nnero.mcmc.ReionizationLikelihood(parameters: list[str], *, classifier: Classifier, regressor: Regressor)

Bases: Likelihood

class nnero.mcmc.UVLFLikelihood(parameters: list[str], *, parameters_xi: ndarray | None = None, xi: ndarray | None = None, k: ndarray | None = None, pk: ndarray | None = None, precompute: bool = False)

Bases: Likelihood

Likelihood for the UV luminosity functions.

Parameters:

• k (np.ndarray) – Array of modes on which the matter power spectrum is given (in 1/Mpc).

• pk (np.ndarray) – Matter power spectrum.

get_k_max(x) → None | float

nnero.mcmc.initialise_walkers(theta_min: ndarray, theta_max: ndarray, xi: ndarray, likelihoods: list[Likelihood], n_walkers: int = 64, **kwargs)

nnero.mcmc.log_likelihood(theta: ndarray, xi: ndarray, likelihoods: list[Likelihood], **kwargs) → ndarray

Compute the log Likelihood values.

Parameters:

• theta (np.ndarray) – Varying parameters.

• xi (np.ndarray) – Extra fixed parameters.

• likelihoods (list[Likelihood]) – The likelihoods to evaluate for the fit.

Returns:

Values of the log Likelihood for each chain.

Return type:

np.ndarray

nnero.mcmc.log_prior(theta: ndarray, theta_min: ndarray, theta_max: ndarray, **kwargs) → ndarray

Natural logarithm of the prior

assume flat prior except for the parameters for which a covariance matrix and average value are given

Parameters:

• theta: (n, d) ndarray

  parameters d is the dimension of the vector parameter n is the number of vector parameter treated at once

• theta_min: (d) ndarray

  minimum value of the parameters allowed

• theta_max:

  maximum value of the parameters allowed

kwargs:

• mask: optional, (d) ndarray

  where the covariance matrix applies the mask should have p Trues and d-p False with p the dimension of the covariance matrix if cov and my given with dim d then mask still optional

• mu: optional, (p) ndarray

  average value of the gaussian distribution

• cov: optional, (p, p) ndarray

  covariance matrix

nnero.mcmc.log_probability(theta: ndarray, xi: ndarray, theta_min: ndarray, theta_max: ndarray, likelihoods: list[Likelihood], **kwargs) → ndarray