WangCubic#

class cuqi.testproblem.WangCubic(noise_std=1, prior=None, data=None)#

Two parameters and one observation cubic test problem.

Parameters:

  • noise_std (scalar) – Standard deviation of the noise

  • prior (cuqi.distribution.Distribution) – Distribution of the prior

  • data (scalar) – Observed data

Notes

Based on Section 3.3.2 in Wang (2015): Z. Wang, “An Optimization Based Algorithm for Bayesian Inference”. Master thesis. MIT. 2015 https://dspace.mit.edu/bitstream/handle/1721.1/98815/921147308-MIT.pdf?sequence=1&isAllowed=y

__init__(noise_std=1, prior=None, data=None)#

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__([noise_std, prior, data])

get_components()

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

data

Extract the observed data from likelihood

likelihood

The likelihood function.

model

Extract the cuqi model from likelihood.

posterior

Create posterior distribution from likelihood and prior.

prior

The prior distribution