bilby.bilby_mcmc.proposals.PriorProposal

class bilby.bilby_mcmc.proposals.PriorProposal(priors, weight=1, subset=None)[source]

Bases: BaseProposal

A proposal using draws from the prior distribution

Note: for priors which use interpolation, this proposal can be problematic as the proposal gets pickled in multiprocessing. Either, use serial processing (npool=1) or fall back to a UniformProposal.

Parameters:

priors: bilby.core.prior.PriorDict: The set of priors
weight: float: Weighting factor
subset: list: A list of keys for which to restrict the proposal to (other parameters will be kept fixed)

__init__(priors, weight=1, subset=None)[source]

__call__(chain, likelihood=None, priors=None)[source]: Call self as a function.

Methods

`__init__`(priors[, weight, subset])
`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

Attributes

`acceptance_ratio`
`accepted`
`n`
`rejected`

static check_dependencies(warn=True)[source]

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)[source]

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.