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Source code for blackjax.adaptation.adjusted_mclmc_adaptation

+import jax
+import jax.numpy as jnp
+from jax.flatten_util import ravel_pytree
+
+from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState
+from blackjax.adaptation.step_size import (
+    DualAveragingAdaptationState,
+    dual_averaging_adaptation,
+)
+from blackjax.diagnostics import effective_sample_size
+from blackjax.util import incremental_value_update, pytree_size
+
+
+[docs] +Lratio_lowerbound = 0.0
+ +
+[docs] +Lratio_upperbound = 2.0
+ + + +
+[docs] +def adjusted_mclmc_find_L_and_step_size( + mclmc_kernel, + num_steps, + state, + rng_key, + target, + frac_tune1=0.1, + frac_tune2=0.1, + frac_tune3=0.0, + diagonal_preconditioning=True, + params=None, + max="avg", + num_windows=1, + tuning_factor=1.3, +): + """ + Finds the optimal value of the parameters for the MH-MCHMC algorithm. + + Parameters + ---------- + mclmc_kernel + The kernel function used for the MCMC algorithm. + num_steps + The number of MCMC steps that will subsequently be run, after tuning. + state + The initial state of the MCMC algorithm. + rng_key + The random number generator key. + target + The target acceptance rate for the step size adaptation. + frac_tune1 + The fraction of tuning for the first step of the adaptation. + frac_tune2 + The fraction of tuning for the second step of the adaptation. + frac_tune3 + The fraction of tuning for the third step of the adaptation. + diagonal_preconditioning + Whether to do diagonal preconditioning (i.e. a mass matrix) + params + Initial params to start tuning from (optional) + max + whether to calculate L from maximum or average eigenvalue. Average is advised. + num_windows + how many iterations of the tuning are carried out + tuning_factor + multiplicative factor for L + + + Returns + ------- + A tuple containing the final state of the MCMC algorithm and the final hyperparameters. + """ + + frac_tune1 /= num_windows + frac_tune2 /= num_windows + frac_tune3 /= num_windows + + dim = pytree_size(state.position) + if params is None: + params = MCLMCAdaptationState( + jnp.sqrt(dim), jnp.sqrt(dim) * 0.2, sqrt_diag_cov=jnp.ones((dim,)) + ) + + part1_key, part2_key = jax.random.split(rng_key, 2) + + for i in range(num_windows): + window_key = jax.random.fold_in(part1_key, i) + (state, params, eigenvector) = adjusted_mclmc_make_L_step_size_adaptation( + kernel=mclmc_kernel, + dim=dim, + frac_tune1=frac_tune1, + frac_tune2=frac_tune2, + target=target, + diagonal_preconditioning=diagonal_preconditioning, + max=max, + tuning_factor=tuning_factor, + )(state, params, num_steps, window_key) + + if frac_tune3 != 0: + for i in range(num_windows): + part2_key = jax.random.fold_in(part2_key, i) + part2_key1, part2_key2 = jax.random.split(part2_key, 2) + + state, params = adjusted_mclmc_make_adaptation_L( + mclmc_kernel, + frac=frac_tune3, + Lfactor=0.5, + max=max, + eigenvector=eigenvector, + )(state, params, num_steps, part2_key1) + + (state, params, _) = adjusted_mclmc_make_L_step_size_adaptation( + kernel=mclmc_kernel, + dim=dim, + frac_tune1=frac_tune1, + frac_tune2=0, + target=target, + fix_L_first_da=True, + diagonal_preconditioning=diagonal_preconditioning, + max=max, + tuning_factor=tuning_factor, + )(state, params, num_steps, part2_key2) + + return state, params
+ + + +
+[docs] +def adjusted_mclmc_make_L_step_size_adaptation( + kernel, + dim, + frac_tune1, + frac_tune2, + target, + diagonal_preconditioning, + fix_L_first_da=False, + max="avg", + tuning_factor=1.0, +): + """Adapts the stepsize and L of the MCLMC kernel. Designed for adjusted MCLMC""" + + def dual_avg_step(fix_L, update_da): + """does one step of the dynamics and updates the estimate of the posterior size and optimal stepsize""" + + def step(iteration_state, weight_and_key): + mask, rng_key = weight_and_key + ( + previous_state, + params, + (adaptive_state, step_size_max), + previous_weight_and_average, + ) = iteration_state + + avg_num_integration_steps = params.L / params.step_size + + state, info = kernel( + rng_key=rng_key, + state=previous_state, + avg_num_integration_steps=avg_num_integration_steps, + step_size=params.step_size, + sqrt_diag_cov=params.sqrt_diag_cov, + ) + + # step updating + success, state, step_size_max, energy_change = handle_nans( + previous_state, + state, + params.step_size, + step_size_max, + info.energy, + ) + + with_mask = lambda x, y: mask * x + (1 - mask) * y + + log_step_size, log_step_size_avg, step, avg_error, mu = update_da( + adaptive_state, info.acceptance_rate + ) + + adaptive_state = DualAveragingAdaptationState( + with_mask(log_step_size, adaptive_state.log_step_size), + with_mask(log_step_size_avg, adaptive_state.log_step_size_avg), + with_mask(step, adaptive_state.step), + with_mask(avg_error, adaptive_state.avg_error), + with_mask(mu, adaptive_state.mu), + ) + + step_size = jax.lax.clamp( + 1e-5, jnp.exp(adaptive_state.log_step_size), params.L / 1.1 + ) + adaptive_state = adaptive_state._replace(log_step_size=jnp.log(step_size)) + + x = ravel_pytree(state.position)[0] + + # update the running average of x, x^2 + previous_weight_and_average = incremental_value_update( + expectation=jnp.array([x, jnp.square(x)]), + incremental_val=previous_weight_and_average, + weight=(1 - mask) * success * step_size, + zero_prevention=mask, + ) + + params = params._replace(step_size=with_mask(step_size, params.step_size)) + if not fix_L: + params = params._replace( + L=with_mask(params.L * (step_size / params.step_size), params.L), + ) + + state_position = state.position + + return ( + state, + params, + (adaptive_state, step_size_max), + previous_weight_and_average, + ), ( + info, + state_position, + ) + + return step + + def step_size_adaptation(mask, state, params, keys, fix_L, initial_da, update_da): + return jax.lax.scan( + dual_avg_step(fix_L, update_da), + init=( + state, + params, + (initial_da(params.step_size), jnp.inf), # step size max + (0.0, jnp.array([jnp.zeros(dim), jnp.zeros(dim)])), + ), + xs=(mask, keys), + ) + + def L_step_size_adaptation(state, params, num_steps, rng_key): + num_steps1, num_steps2 = int(num_steps * frac_tune1), int( + num_steps * frac_tune2 + ) + + check_key, rng_key = jax.random.split(rng_key, 2) + + rng_key_pass1, rng_key_pass2 = jax.random.split(rng_key, 2) + L_step_size_adaptation_keys_pass1 = jax.random.split( + rng_key_pass1, num_steps1 + num_steps2 + ) + L_step_size_adaptation_keys_pass2 = jax.random.split(rng_key_pass2, num_steps1) + + # determine which steps to ignore in the streaming average + mask = 1 - jnp.concatenate((jnp.zeros(num_steps1), jnp.ones(num_steps2))) + + initial_da, update_da, final_da = dual_averaging_adaptation(target=target) + + ( + (state, params, (dual_avg_state, step_size_max), (_, average)), + (info, position_samples), + ) = step_size_adaptation( + mask, + state, + params, + L_step_size_adaptation_keys_pass1, + fix_L=fix_L_first_da, + initial_da=initial_da, + update_da=update_da, + ) + + final_stepsize = final_da(dual_avg_state) + params = params._replace(step_size=final_stepsize) + + # determine L + eigenvector = None + if num_steps2 != 0.0: + x_average, x_squared_average = average[0], average[1] + variances = x_squared_average - jnp.square(x_average) + + if max == "max": + contract = lambda x: jnp.sqrt(jnp.max(x) * dim) * tuning_factor + + elif max == "avg": + contract = lambda x: jnp.sqrt(jnp.sum(x)) * tuning_factor + + else: + raise ValueError("max should be either 'max' or 'avg'") + + change = jax.lax.clamp( + Lratio_lowerbound, + contract(variances) / params.L, + Lratio_upperbound, + ) + params = params._replace( + L=params.L * change, step_size=params.step_size * change + ) + if diagonal_preconditioning: + params = params._replace( + sqrt_diag_cov=jnp.sqrt(variances), L=jnp.sqrt(dim) + ) + + initial_da, update_da, final_da = dual_averaging_adaptation(target=target) + ( + (state, params, (dual_avg_state, step_size_max), (_, average)), + (info, params_history), + ) = step_size_adaptation( + jnp.ones(num_steps1), + state, + params, + L_step_size_adaptation_keys_pass2, + fix_L=True, + update_da=update_da, + initial_da=initial_da, + ) + + params = params._replace(step_size=final_da(dual_avg_state)) + + return state, params, eigenvector + + return L_step_size_adaptation
+ + + +
+[docs] +def adjusted_mclmc_make_adaptation_L( + kernel, frac, Lfactor, max="avg", eigenvector=None +): + """determine L by the autocorrelations (around 10 effective samples are needed for this to be accurate)""" + + def adaptation_L(state, params, num_steps, key): + num_steps = int(num_steps * frac) + adaptation_L_keys = jax.random.split(key, num_steps) + + def step(state, key): + next_state, _ = kernel( + rng_key=key, + state=state, + step_size=params.step_size, + avg_num_integration_steps=params.L / params.step_size, + sqrt_diag_cov=params.sqrt_diag_cov, + ) + return next_state, next_state.position + + state, samples = jax.lax.scan( + f=step, + init=state, + xs=adaptation_L_keys, + ) + + if max == "max": + contract = jnp.min + else: + contract = jnp.mean + + flat_samples = jax.vmap(lambda x: ravel_pytree(x)[0])(samples) + + if eigenvector is not None: + flat_samples = jnp.expand_dims( + jnp.einsum("ij,j", flat_samples, eigenvector), 1 + ) + + # number of effective samples per 1 actual sample + ess = contract(effective_sample_size(flat_samples[None, ...])) / num_steps + + return state, params._replace( + L=jnp.clip( + Lfactor * params.L / jnp.mean(ess), max=params.L * Lratio_upperbound + ) + ) + + return adaptation_L
+ + + +
+[docs] +def handle_nans(previous_state, next_state, step_size, step_size_max, kinetic_change): + """if there are nans, let's reduce the stepsize, and not update the state. The + function returns the old state in this case.""" + + reduced_step_size = 0.8 + p, unravel_fn = ravel_pytree(next_state.position) + nonans = jnp.all(jnp.isfinite(p)) + state, step_size, kinetic_change = jax.tree_util.tree_map( + lambda new, old: jax.lax.select(nonans, jnp.nan_to_num(new), old), + (next_state, step_size_max, kinetic_change), + (previous_state, step_size * reduced_step_size, 0.0), + ) + + return nonans, state, step_size, kinetic_change
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Source code for blackjax.mcmc.adjusted_mclmc

+# Copyright 2020- The Blackjax Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Public API for the Metropolis Hastings Microcanonical Hamiltonian Monte Carlo (MHMCHMC) Kernel. This is closely related to the Microcanonical Langevin Monte Carlo (MCLMC) Kernel, which is an unadjusted method. This kernel adds a Metropolis-Hastings correction to the MCLMC kernel. It also only refreshes the momentum variable after each MH step, rather than during the integration of the trajectory. Hence "Hamiltonian" and not "Langevin"."""
+from typing import Callable, Union
+
+import jax
+import jax.numpy as jnp
+
+import blackjax.mcmc.integrators as integrators
+from blackjax.base import SamplingAlgorithm
+from blackjax.mcmc.dynamic_hmc import DynamicHMCState, halton_sequence
+from blackjax.mcmc.hmc import HMCInfo
+from blackjax.mcmc.proposal import static_binomial_sampling
+from blackjax.types import Array, ArrayLikeTree, ArrayTree, PRNGKey
+from blackjax.util import generate_unit_vector
+
+__all__ = ["init", "build_kernel", "as_top_level_api"]
+
+
+
+[docs] +def init(position: ArrayLikeTree, logdensity_fn: Callable, random_generator_arg: Array): + logdensity, logdensity_grad = jax.value_and_grad(logdensity_fn)(position) + return DynamicHMCState(position, logdensity, logdensity_grad, random_generator_arg)
+ + + +
+[docs] +def build_kernel( + integration_steps_fn, + integrator: Callable = integrators.isokinetic_mclachlan, + divergence_threshold: float = 1000, + next_random_arg_fn: Callable = lambda key: jax.random.split(key)[1], + sqrt_diag_cov=1.0, +): + """Build a Dynamic MHMCHMC kernel where the number of integration steps is chosen randomly. + + Parameters + ---------- + integrator + The integrator to use to integrate the Hamiltonian dynamics. + divergence_threshold + Value of the difference in energy above which we consider that the transition is divergent. + next_random_arg_fn + Function that generates the next `random_generator_arg` from its previous value. + integration_steps_fn + Function that generates the next pseudo or quasi-random number of integration steps in the + sequence, given the current `random_generator_arg`. Needs to return an `int`. + + 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. + """ + + def kernel( + rng_key: PRNGKey, + state: DynamicHMCState, + logdensity_fn: Callable, + step_size: float, + L_proposal_factor: float = jnp.inf, + ) -> tuple[DynamicHMCState, HMCInfo]: + """Generate a new sample with the MHMCHMC kernel.""" + + num_integration_steps = integration_steps_fn(state.random_generator_arg) + + key_momentum, key_integrator = jax.random.split(rng_key, 2) + momentum = generate_unit_vector(key_momentum, state.position) + proposal, info, _ = adjusted_mclmc_proposal( + integrator=integrators.with_isokinetic_maruyama( + integrator(logdensity_fn=logdensity_fn, sqrt_diag_cov=sqrt_diag_cov) + ), + step_size=step_size, + L_proposal_factor=L_proposal_factor * (num_integration_steps * step_size), + num_integration_steps=num_integration_steps, + divergence_threshold=divergence_threshold, + )( + key_integrator, + integrators.IntegratorState( + state.position, momentum, state.logdensity, state.logdensity_grad + ), + ) + + return ( + DynamicHMCState( + proposal.position, + proposal.logdensity, + proposal.logdensity_grad, + next_random_arg_fn(state.random_generator_arg), + ), + info, + ) + + return kernel
+ + + +
+[docs] +def as_top_level_api( + logdensity_fn: Callable, + step_size: float, + L_proposal_factor: float = jnp.inf, + sqrt_diag_cov=1.0, + *, + divergence_threshold: int = 1000, + integrator: Callable = integrators.isokinetic_mclachlan, + next_random_arg_fn: Callable = lambda key: jax.random.split(key)[1], + integration_steps_fn: Callable = lambda key: jax.random.randint(key, (), 1, 10), +) -> SamplingAlgorithm: + """Implements the (basic) user interface for the dynamic MHMCHMC kernel. + + Parameters + ---------- + logdensity_fn + The log-density function we wish to draw samples from. + step_size + The value to use for the step size in the symplectic integrator. + divergence_threshold + The absolute value of the difference in energy between two states above + which we say that the transition is divergent. The default value is + commonly found in other libraries, and yet is arbitrary. + integrator + (algorithm parameter) The symplectic integrator to use to integrate the trajectory. + next_random_arg_fn + Function that generates the next `random_generator_arg` from its previous value. + integration_steps_fn + Function that generates the next pseudo or quasi-random number of integration steps in the + sequence, given the current `random_generator_arg`. + + + Returns + ------- + A ``SamplingAlgorithm``. + """ + + kernel = build_kernel( + integration_steps_fn=integration_steps_fn, + integrator=integrator, + next_random_arg_fn=next_random_arg_fn, + sqrt_diag_cov=sqrt_diag_cov, + divergence_threshold=divergence_threshold, + ) + + def init_fn(position: ArrayLikeTree, rng_key: Array): + return init(position, logdensity_fn, rng_key) + + def update_fn(rng_key: PRNGKey, state): + return kernel( + rng_key, + state, + logdensity_fn, + step_size, + L_proposal_factor, + ) + + return SamplingAlgorithm(init_fn, update_fn) # type: ignore[arg-type]
+ + + +def adjusted_mclmc_proposal( + integrator: Callable, + step_size: Union[float, ArrayLikeTree], + L_proposal_factor: float, + num_integration_steps: int = 1, + divergence_threshold: float = 1000, + *, + sample_proposal: Callable = static_binomial_sampling, +) -> Callable: + """Vanilla MHMCHMC algorithm. + + The algorithm integrates the trajectory applying a integrator + `num_integration_steps` times in one direction to get a proposal and uses a + Metropolis-Hastings acceptance step to either reject or accept this + proposal. This is what people usually refer to when they talk about "the + HMC algorithm". + + Parameters + ---------- + integrator + integrator used to build the trajectory step by step. + kinetic_energy + Function that computes the kinetic energy. + step_size + Size of the integration step. + num_integration_steps + Number of times we run the integrator to build the trajectory + divergence_threshold + Threshold above which we say that there is a divergence. + + Returns + ------- + A kernel that generates a new chain state and information about the transition. + + """ + + def step(i, vars): + state, kinetic_energy, rng_key = vars + rng_key, next_rng_key = jax.random.split(rng_key) + next_state, next_kinetic_energy = integrator( + state, step_size, L_proposal_factor, rng_key + ) + + return next_state, kinetic_energy + next_kinetic_energy, next_rng_key + + def build_trajectory(state, num_integration_steps, rng_key): + return jax.lax.fori_loop( + 0 * num_integration_steps, num_integration_steps, step, (state, 0, rng_key) + ) + + def generate( + rng_key, state: integrators.IntegratorState + ) -> tuple[integrators.IntegratorState, HMCInfo, ArrayTree]: + """Generate a new chain state.""" + end_state, kinetic_energy, rng_key = build_trajectory( + state, num_integration_steps, rng_key + ) + + new_energy = -end_state.logdensity + delta_energy = -state.logdensity + end_state.logdensity - kinetic_energy + delta_energy = jnp.where(jnp.isnan(delta_energy), -jnp.inf, delta_energy) + is_diverging = -delta_energy > divergence_threshold + sampled_state, info = sample_proposal(rng_key, delta_energy, state, end_state) + do_accept, p_accept, other_proposal_info = info + + info = HMCInfo( + state.momentum, + p_accept, + do_accept, + is_diverging, + new_energy, + end_state, + num_integration_steps, + ) + + return sampled_state, info, other_proposal_info + + return generate + + +def rescale(mu): + """returns s, such that + round(U(0, 1) * s + 0.5) + has expected value mu. + """ + k = jnp.floor(2 * mu - 1) + x = k * (mu - 0.5 * (k + 1)) / (k + 1 - mu) + return k + x + + +def trajectory_length(t, mu): + s = rescale(mu) + return jnp.rint(0.5 + halton_sequence(t) * s) +
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blackjax.adaptation.adjusted_mclmc_adaptation#

+
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Attributes#

+ +
+
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Functions#

+
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adjusted_mclmc_find_L_and_step_size(mclmc_kernel, ...)

Finds the optimal value of the parameters for the MH-MCHMC algorithm.

adjusted_mclmc_make_L_step_size_adaptation(kernel, ...)

Adapts the stepsize and L of the MCLMC kernel. Designed for adjusted MCLMC

adjusted_mclmc_make_adaptation_L(kernel, frac, Lfactor)

determine L by the autocorrelations (around 10 effective samples are needed for this to be accurate)

handle_nans(previous_state, next_state, step_size, ...)

if there are nans, let's reduce the stepsize, and not update the state. The

+
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Module Contents#

+
+
+Lratio_lowerbound = 0.0[source]#
+
+ +
+
+Lratio_upperbound = 2.0[source]#
+
+ +
+
+adjusted_mclmc_find_L_and_step_size(mclmc_kernel, num_steps, state, rng_key, target, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, diagonal_preconditioning=True, params=None, max='avg', num_windows=1, tuning_factor=1.3)[source]#
+

Finds the optimal value of the parameters for the MH-MCHMC algorithm.

+
+
Parameters:
+
    +
  • mclmc_kernel – The kernel function used for the MCMC algorithm.

  • +
  • num_steps – The number of MCMC steps that will subsequently be run, after tuning.

  • +
  • state – The initial state of the MCMC algorithm.

  • +
  • rng_key – The random number generator key.

  • +
  • target – The target acceptance rate for the step size adaptation.

  • +
  • frac_tune1 – The fraction of tuning for the first step of the adaptation.

  • +
  • frac_tune2 – The fraction of tuning for the second step of the adaptation.

  • +
  • frac_tune3 – The fraction of tuning for the third step of the adaptation.

  • +
  • diagonal_preconditioning – Whether to do diagonal preconditioning (i.e. a mass matrix)

  • +
  • params – Initial params to start tuning from (optional)

  • +
  • max – whether to calculate L from maximum or average eigenvalue. Average is advised.

  • +
  • num_windows – how many iterations of the tuning are carried out

  • +
  • tuning_factor – multiplicative factor for L

  • +
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Return type:
+

A tuple containing the final state of the MCMC algorithm and the final hyperparameters.

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+adjusted_mclmc_make_L_step_size_adaptation(kernel, dim, frac_tune1, frac_tune2, target, diagonal_preconditioning, fix_L_first_da=False, max='avg', tuning_factor=1.0)[source]#
+

Adapts the stepsize and L of the MCLMC kernel. Designed for adjusted MCLMC

+
+ +
+
+adjusted_mclmc_make_adaptation_L(kernel, frac, Lfactor, max='avg', eigenvector=None)[source]#
+

determine L by the autocorrelations (around 10 effective samples are needed for this to be accurate)

+
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+handle_nans(previous_state, next_state, step_size, step_size_max, kinetic_change)[source]#
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if there are nans, let’s reduce the stepsize, and not update the state. The +function returns the old state in this case.

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blackjax.mcmc.adjusted_mclmc

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blackjax.mcmc.adjusted_mclmc#

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Public API for the Metropolis Hastings Microcanonical Hamiltonian Monte Carlo (MHMCHMC) Kernel. This is closely related to the Microcanonical Langevin Monte Carlo (MCLMC) Kernel, which is an unadjusted method. This kernel adds a Metropolis-Hastings correction to the MCLMC kernel. It also only refreshes the momentum variable after each MH step, rather than during the integration of the trajectory. Hence “Hamiltonian” and not “Langevin”.

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Functions#

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init(position, logdensity_fn, random_generator_arg)

build_kernel(integration_steps_fn[, integrator, ...])

Build a Dynamic MHMCHMC kernel where the number of integration steps is chosen randomly.

as_top_level_api([1], integration_steps_fn, 1, ...)

Implements the (basic) user interface for the dynamic MHMCHMC kernel.

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Module Contents#

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+init(position: blackjax.types.ArrayLikeTree, logdensity_fn: Callable, random_generator_arg: blackjax.types.Array)[source]#
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+ +
+
+build_kernel(integration_steps_fn, integrator: Callable = integrators.isokinetic_mclachlan, divergence_threshold: float = 1000, next_random_arg_fn: Callable = lambda key: ..., sqrt_diag_cov=1.0)[source]#
+

Build a Dynamic MHMCHMC kernel where the number of integration steps is chosen randomly.

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+
Parameters:
+
    +
  • integrator – The integrator to use to integrate the Hamiltonian dynamics.

  • +
  • divergence_threshold – Value of the difference in energy above which we consider that the transition is divergent.

  • +
  • next_random_arg_fn – Function that generates the next random_generator_arg from its previous value.

  • +
  • integration_steps_fn – Function that generates the next pseudo or quasi-random number of integration steps in the +sequence, given the current random_generator_arg. Needs to return an int.

  • +
+
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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.

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+

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+as_top_level_api(logdensity_fn: Callable, step_size: float, L_proposal_factor: float = jnp.inf, sqrt_diag_cov=1.0, *, divergence_threshold: int = 1000, integrator: Callable = integrators.isokinetic_mclachlan, next_random_arg_fn: Callable = lambda key: ..., integration_steps_fn: Callable = lambda key: ...) blackjax.base.SamplingAlgorithm[source]#
+

Implements the (basic) user interface for the dynamic MHMCHMC kernel.

+
+
Parameters:
+
    +
  • logdensity_fn – The log-density function we wish to draw samples from.

  • +
  • step_size – The value to use for the step size in the symplectic integrator.

  • +
  • divergence_threshold – The absolute value of the difference in energy between two states above +which we say that the transition is divergent. The default value is +commonly found in other libraries, and yet is arbitrary.

  • +
  • integrator – (algorithm parameter) The symplectic integrator to use to integrate the trajectory.

  • +
  • next_random_arg_fn – Function that generates the next random_generator_arg from its previous value.

  • +
  • integration_steps_fn – Function that generates the next pseudo or quasi-random number of integration steps in the +sequence, given the current random_generator_arg.

  • +
+
+
Return type:
+

A SamplingAlgorithm.

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+ + \ No newline at end of file diff --git a/autoapi/blackjax/mcmc/barker/index.html b/autoapi/blackjax/mcmc/barker/index.html index 3e63ecb1d..e9d6b2199 100644 --- a/autoapi/blackjax/mcmc/barker/index.html +++ b/autoapi/blackjax/mcmc/barker/index.html @@ -16,8 +16,8 @@ document.documentElement.dataset.mode = localStorage.getItem("mode") || ""; document.documentElement.dataset.theme = localStorage.getItem("theme") || ""; -

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