LinearRTO

class cuqi.experimental.mcmc.LinearRTO(target=None, initial_point=None, maxit=10, tol=1e-06, **kwargs)

Linear RTO (Randomize-Then-Optimize) sampler.

Samples posterior related to the inverse problem with Gaussian likelihood and prior, and where the forward model is linear or more generally affine.

Parameters:

• target (cuqi.distribution.Posterior, cuqi.distribution.MultipleLikelihoodPosterior or 5-dimensional tuple.) –

  If target is of type cuqi.distribution.Posterior or cuqi.distribution.MultipleLikelihoodPosterior, it represents the posterior distribution. If target is a 5-dimensional tuple, it assumes the following structure: (data, model, L_sqrtprec, P_mean, P_sqrtrec)

  Here: data: is a m-dimensional numpy array containing the measured data. model: is a m by n dimensional matrix, AffineModel or LinearModel representing the forward model. L_sqrtprec: is the squareroot of the precision matrix of the Gaussian likelihood. P_mean: is the prior mean. P_sqrtprec: is the squareroot of the precision matrix of the Gaussian mean.

• initial_point (np.ndarray) – Initial point for the sampler. Optional.

• maxit (int) – Maximum number of iterations of the inner CGLS solver. Optional.

• tol (float) – Tolerance of the inner CGLS solver. Optional.

• callback (callable, optional) – A function that will be called after each sampling step. It can be useful for monitoring the sampler during sampling. The function should take three arguments: the sampler object, the index of the current sampling step, the total number of requested samples. The last two arguments are integers. An example of the callback function signature is: callback(sampler, sample_index, num_of_samples).

__init__(target=None, initial_point=None, maxit=10, tol=1e-06, **kwargs)

Initializer for abstract base class for all samplers.

Any subclassing samplers should simply store input parameters as part of the __init__ method.

The actual initialization of the sampler should be done in the _initialize method.

Parameters:

• target (cuqi.density.Density) – The target density.

• initial_point (array-like, optional) – The initial point for the sampler. If not given, the sampler will choose an initial point.

• callback (callable, optional) – A function that will be called after each sampling step. It can be useful for monitoring the sampler during sampling. The function should take three arguments: the sampler object, the index of the current sampling step, the total number of requested samples. The last two arguments are integers. An example of the callback function signature is: callback(sampler, sample_index, num_of_samples).

Methods

__init__([target, initial_point, maxit, tol])	Initializer for abstract base class for all samplers.
get_history()	Return the history of the sampler.
get_samples()	Return the samples.
get_state()	Return the state of the sampler.
initialize()	Initialize the sampler by setting and allocating the state and history before sampling starts.
load_checkpoint(path)	Load the state of the sampler from a file.
reinitialize()	Re-initialize the sampler.
sample(Ns[, batch_size, sample_path])	Sample Ns samples from the target density.
save_checkpoint(path)	Save the state of the sampler to a file.
set_history(history)	Set the history of the sampler.
set_state(state)	Set the state of the sampler.
step()	Perform one step of the sampler by transitioning the current point to a new point according to the sampler's transition kernel.
tune(skip_len, update_count)	Tune the parameters of the sampler.
validate_target()	Validate the target is compatible with the sampler.
warmup(Nb[, tune_freq])	Warmup the sampler by drawing Nb samples.

Attributes

dim	Dimension of the target density.
geometry	Geometry of the target density.
likelihood
likelihoods
model
models
prior
target	Return the target density.