Abel1D#
- class cuqi.testproblem.Abel1D(dim=128, endpoint=1, field_type=None, field_params=None, KL_map=None, KL_imap=None, SNR=100)#
1D Abel test problem. 1D model of rotationally symmetric computed tomography.
- Parameters:
dim (int) – size of the grid for the problem
endpoint (float) – Location of end-point of grid.
field_type (str or cuqi.geometry.Geometry) – Field type of domain.
KL_map (lambda function) – Mapping used to modify field.
KL_imap (lambda function) – Inverse of KL map.
SNR (int) – Signal-to-noise ratio
- data#
Generated (noisy) data
- Type:
ndarray
- model#
Abel 1D model
- Type:
- prior#
Distribution of the prior
- likelihood#
Likelihood function
- exactSolution#
Exact solution (ground truth)
- Type:
ndarray
- exactData#
Noise free data
- Type:
ndarray
- MAP()#
Compute MAP estimate of posterior. NB: Requires prior to be defined.
- sample_posterior(Ns)#
Sample Ns samples of the posterior. NB: Requires prior to be defined.
- __init__(dim=128, endpoint=1, field_type=None, field_params=None, KL_map=None, KL_imap=None, SNR=100)#
Methods
MAP([disp, x0])Compute the Maximum A Posteriori (MAP) estimate of the posterior.
ML([disp, x0])Compute the Maximum Likelihood (ML) estimate of the posterior.
UQ([Ns, Nb, percent, exact, experimental])Run an Uncertainty Quantification (UQ) analysis on the Bayesian problem and provide a summary of the results.
__init__([dim, endpoint, field_type, ...])Method that returns the model, the data and additional information to be used in formulating the Bayesian problem.
sample_posterior(Ns[, Nb, callback, ...])Sample the posterior.
sample_prior(Ns[, callback])Sample the prior distribution.
set_data(**kwargs)Set the data of the problem.
Attributes
Extract the observed data from likelihood
The likelihood function.
Extract the cuqi model from likelihood.
Create posterior distribution from likelihood and prior.
The prior distribution