Adaptive Covariance MC

class pints.AdaptiveCovarianceMC(x0, sigma0=None)

Base class for single chain MCMC methods that globally adapt a proposal covariance matrix when running, in order to control the acceptance rate.

Each subclass should provide a method _generate_proposal() that will be called by ask().

Adaptation is implemented with three methods, which are called in sequence, at the end of every tell(): _adapt_mu(), _adapt_sigma(), and _adapt_internal(). A basic implementation is provided for each, which extending methods can choose to override.

Extends SingleChainMCMC, does not use sensitivities.

acceptance_rate()

Returns the current (measured) acceptance rate.

ask()

See SingleChainMCMC.ask().

eta()

Returns eta which controls the rate of adaptation decay adaptations**(-eta), where eta > 0 to ensure asymptotic ergodicity.

in_initial_phase()

See pints.MCMCSampler.in_initial_phase().

n_hyper_parameters()

See TunableMethod.n_hyper_parameters().

classmethod name()

Returns this method’s full name.

needs_initial_phase()

See pints.MCMCSampler.needs_initial_phase().

needs_sensitivities()

Returns True if this methods needs sensitivities to be passed in to tell along with the evaluated logpdf.

replace(current, current_log_pdf, proposed=None)

See pints.SingleChainMCMC.replace().

set_eta(eta)

Updates eta which controls the rate of adaptation decay adaptations**(-eta), where eta > 0 to ensure asymptotic ergodicity.

set_hyper_parameters(x)

The hyper-parameter vector is [eta].

See TunableMethod.set_hyper_parameters().

set_initial_phase(initial_phase)

See pints.MCMCSampler.set_initial_phase().

set_target_acceptance_rate(rate=0.234)

Sets the target acceptance rate.

target_acceptance_rate()

Returns the target acceptance rate.

tell(fx)

See pints.SingleChainMCMC.tell().