blackjax.mcmc.elliptical_slice

blackjax.mcmc.elliptical_slice#

Public API for the Elliptical Slice sampling Kernel

Classes#

`EllipSliceState`	State of the Elliptical Slice sampling algorithm.
`EllipSliceInfo`	Additional information on the Elliptical Slice sampling chain.

Functions#

`init`(position, logdensity_fn)
`build_kernel`(cov_matrix, mean)	Build an Elliptical Slice sampling kernel [MAM10].
`as_top_level_api`(→ blackjax.base.SamplingAlgorithm)	Implements the (basic) user interface for the Elliptical Slice sampling kernel.

Module Contents#

class EllipSliceState[source]#

State of the Elliptical Slice sampling algorithm.

position: Current position of the chain.
logdensity: Current value of the logdensity (evaluated at current position).

position: blackjax.types.ArrayTree[source]#

logdensity: blackjax.types.ArrayTree[source]#

class EllipSliceInfo[source]#

Additional information on the Elliptical Slice sampling chain.

This additional information can be used for debugging or computing diagnostics.

momentum: The latent momentum variable returned at the end of the transition.
theta: A value between [-2pi, 2pi] identifying points in the ellipsis drawn from the positon and mommentum variables. This value indicates the theta value of the accepted proposal.
subiter: Number of sub iterations needed to accept a proposal. The more subiterations needed the less efficient the algorithm will be, and the more dependent the new value is likely to be to the previous value.

momentum: blackjax.types.ArrayTree[source]#

theta: float[source]#

subiter: int[source]#

init(position: blackjax.types.ArrayLikeTree, logdensity_fn: Callable)[source]#

build_kernel(cov_matrix: blackjax.types.Array, mean: blackjax.types.Array)[source]#

Build an Elliptical Slice sampling kernel [MAM10].

Parameters:

cov_matrix – The value of the covariance matrix of the gaussian prior distribution from the posterior we wish to sample.

Returns:

A kernel that takes a rng_key and a Pytree that contains the current state
of the chain and that returns a new state of the chain along with
information about the transition.

as_top_level_api(loglikelihood_fn: Callable, *, mean: blackjax.types.Array, cov: blackjax.types.Array) → blackjax.base.SamplingAlgorithm[source]#

Implements the (basic) user interface for the Elliptical Slice sampling kernel.

Examples

A new Elliptical Slice sampling kernel can be initialized and used with the following code:

ellip_slice = blackjax.elliptical_slice(loglikelihood_fn, cov_matrix)
state = ellip_slice.init(position)
new_state, info = ellip_slice.step(rng_key, state)

We can JIT-compile the step function for better performance

step = jax.jit(ellip_slice.step)
new_state, info = step(rng_key, state)

Parameters:

loglikelihood_fn – Only the log likelihood function from the posterior distributon we wish to sample.
cov_matrix – The value of the covariance matrix of the gaussian prior distribution from the posterior we wish to sample.

Return type:

A SamplingAlgorithm.