MH#
- class cuqi.sampler.MH(target, proposal=None, scale=None, x0=None, dim=None, **kwargs)#
Metropolis Hastings sampler.
Allows sampling of a target distribution by random-walk sampling of a proposal distribution along with an accept/reject step.
- Parameters:
target (cuqi.distribution.Distribution or lambda function) – The target distribution to sample. Custom logpdfs are supported by using a
cuqi.distribution.UserDefinedDistribution.proposal (cuqi.distribution.Distribution or callable method) – The proposal to sample from. If a callable method it should provide a single independent sample from proposal distribution. Defaults to a Gaussian proposal. Optional.
scale (float) – Scale parameter used to define correlation between previous and proposed sample in random-walk. Optional.
x0 (ndarray) – Initial parameters. Optional
dim (int) – Dimension of parameter space. Required if target and proposal are callable functions. Optional.
callback (callable, Optional) – If set this function will be called after every sample. The signature of the callback function is callback(sample, sample_index), where sample is the current sample and sample_index is the index of the sample. An example is shown in demos/demo31_callback.py.
Example
# Parameters dim = 5 # Dimension of distribution mu = np.arange(dim) # Mean of Gaussian std = 1 # standard deviation of Gaussian # Logpdf function logpdf_func = lambda x: -1/(std**2)*np.sum((x-mu)**2) # Define distribution from logpdf as UserDefinedDistribution (sample and gradients also supported) target = cuqi.distribution.UserDefinedDistribution(dim=dim, logpdf_func=logpdf_func) # Set up sampler sampler = cuqi.sampler.MH(target, scale=1) # Sample samples = sampler.sample(2000)
- __init__(target, proposal=None, scale=None, x0=None, dim=None, **kwargs)#
Metropolis-Hastings (MH) sampler. Default (if proposal is None) is random walk MH with proposal that is Gaussian with identity covariance
Methods
__init__(target[, proposal, scale, x0, dim])Metropolis-Hastings (MH) sampler.
sample(N[, Nb])sample_adapt(N[, Nb])single_update(x_t, target_eval_t)step(x)Perform a single MCMC step
step_tune(x, *args, **kwargs)Perform a single MCMC step and tune the sampler.
tune()Tune the sampler parameters.
Attributes