pymc.DEMetropolis#

class pymc.DEMetropolis(*args, **kwargs)[source]#

Differential Evolution Metropolis sampling step.

Parameters

lamb: float: Lambda parameter of the DE proposal mechanism. Defaults to 2.38 / sqrt(2 * ndim)
vars: list: List of variables for sampler
S: standard deviation or covariance matrix: Some measure of variance to parameterize proposal distribution
proposal_dist: function: Function that returns zero-mean deviates when parameterized with S (and n). Defaults to Uniform(-S,+S).
scaling: scalar or array: Initial scale factor for epsilon. Defaults to 0.001
tune: str: Which hyperparameter to tune. Defaults to None, but can also be ‘scaling’ or ‘lambda’.
tune_interval: int: The frequency of tuning. Defaults to 100 iterations.
model: PyMC Model: Optional model for sampling step. Defaults to None (taken from context).
mode: string or `Mode` instance.: compilation mode passed to PyTensor functions

References

Braak2006: Cajo C.F. ter Braak (2006). A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces. Statistics and Computing link

Methods

`DEMetropolis.__init__`([vars, S, ...])	Parameters
`DEMetropolis.astep`(q0)	Perform a single sample step in a raveled and concatenated parameter space.
`DEMetropolis.competence`(var, has_grad)
`DEMetropolis.link_population`(population, ...)	Links the sampler to the population.
`DEMetropolis.step`(point)	Perform a single step of the sampler.
`DEMetropolis.stop_tuning`()

Attributes