bilby.bilby_mcmc.proposals.AdaptiveGaussianProposal

class bilby.bilby_mcmc.proposals.AdaptiveGaussianProposal(priors, weight=1, subset=None, sigma=1, scale_init=1.0, stop=100000.0, target_facc=0.234)

Bases: BaseProposal

__init__(priors, weight=1, subset=None, sigma=1, scale_init=1.0, stop=100000.0, target_facc=0.234)

__call__(chain, likelihood=None, priors=None)

Call self as a function.

Methods

__init__(priors[, weight, subset, sigma, ...])
apply_boundaries(point)
apply_periodic_boundary(key, val)
apply_reflective_boundary(key, val)
check_dependencies([warn])	Check the dependencies required to use the proposal
propose(chain)	Propose a new point
update_scale(chain)	The adaptation of the scale follows (35)/(36) of https://arxiv.org/abs/1409.7215

Attributes

acceptance_ratio
accepted
n
rejected

static check_dependencies(warn=True)

Check the dependencies required to use the proposal

Parameters:

warn: bool

If true, print a warning

Returns:

check: bool

If true, dependencies exist

propose(chain)

Propose a new point

This method must be overwritten by implemented proposals. The propose method is called by __call__, then boundaries applied, before returning the proposed point.

Parameters:

chain: bilby.core.sampler.bilby_mcmc.chain.Chain

The chain to use for the proposal

Returns:

proposal: bilby.core.sampler.bilby_mcmc.Sample

The proposed point

log_factor: float

The natural-log of the additional factor entering the acceptance probability to ensure detailed balance. For symmetric proposals, a value of 0 should be returned.

update_scale(chain)

The adaptation of the scale follows (35)/(36) of https://arxiv.org/abs/1409.7215