pymc.sampling.mcmc.init_nuts#

pymc.sampling.mcmc.init_nuts(*, init='auto', chains=1, n_init=500000, model=None, random_seed=None, progressbar=True, jitter_max_retries=10, tune=None, initvals=None, **kwargs)[source]#

Set up the mass matrix initialization for NUTS.

NUTS convergence and sampling speed is extremely dependent on the choice of mass/scaling matrix. This function implements different methods for choosing or adapting the mass matrix.

Parameters:
initstr

Initialization method to use.

  • auto: Choose a default initialization method automatically. Currently, this is jitter+adapt_diag, but this can change in the future. If you depend on the exact behaviour, choose an initialization method explicitly.

  • adapt_diag: Start with a identity mass matrix and then adapt a diagonal based on the variance of the tuning samples. All chains use the test value (usually the prior mean) as starting point.

  • jitter+adapt_diag: Same as adapt_diag, but use test value plus a uniform jitter in [-1, 1] as starting point in each chain.

  • jitter+adapt_diag_grad: An experimental initialization method that uses information from gradients and samples during tuning.

  • advi+adapt_diag: Run ADVI and then adapt the resulting diagonal mass matrix based on the sample variance of the tuning samples.

  • advi: Run ADVI to estimate posterior mean and diagonal mass matrix.

  • advi_map: Initialize ADVI with MAP and use MAP as starting point.

  • map: Use the MAP as starting point. This is discouraged.

  • adapt_full: Adapt a dense mass matrix using the sample covariances. All chains use the test value (usually the prior mean) as starting point.

  • jitter+adapt_full: Same as adapt_full, but use test value plus a uniform jitter in [-1, 1] as starting point in each chain.

chainsint

Number of jobs to start.

initvalsoptional, dict or list of dicts

Dict or list of dicts with initial values to use instead of the defaults from Model.initial_values. The keys should be names of transformed random variables.

n_initint

Number of iterations of initializer. Only works for ‘ADVI’ init methods.

modelModel (optional if in with context)
random_seedint, array_like of int, RandomState or Generator, optional

Seed for the random number generator.

progressbarbool

Whether or not to display a progressbar for advi sampling.

jitter_max_retriesint

Maximum number of repeated attempts (per chain) at creating an initial matrix with uniform jitter that yields a finite probability. This applies to jitter+adapt_diag and jitter+adapt_full init methods.

**kwargskeyword arguments

Extra keyword arguments are forwarded to pymc.NUTS.

Returns:
initial_pointslist

Starting points for each chain.

nuts_samplerpymc.step_methods.NUTS

Instantiated and initialized NUTS sampler object