MetropolisHastings
Inherits From: MonteCarlo
ed.MetropolisHastings
ed.inferences.MetropolisHastings
Defined in edward/inferences/metropolis_hastings.py
.
Metropolis-Hastings (Hastings, 1970; Metropolis, Rosenbluth, Rosenbluth, Teller, & Teller, 1953).
In conditional inference, we infer \(z\) in \(p(z, \beta \mid x)\) while fixing inference over \(\beta\) using another distribution \(q(\beta)\). To calculate the acceptance ratio, MetropolisHastings
uses an estimate of the marginal density,
\(p(x, z) = \mathbb{E}_{q(\beta)} [ p(x, z, \beta) ] \approx p(x, z, \beta^*)\)
leveraging a single Monte Carlo sample, where \(\beta^* \sim q(\beta)\). This is unbiased (and therefore asymptotically exact as a pseudo-marginal method) if \(q(\beta) = p(\beta \mid x)\).
MetropolisHastings
assumes the proposal distribution has the same support as the prior. The auto_transform
attribute in the method initialize()
is not applicable.
mu = Normal(loc=0.0, scale=1.0)
x = Normal(loc=mu, scale=1.0, sample_shape=10)
qmu = Empirical(tf.Variable(tf.zeros(500)))
proposal_mu = Normal(loc=mu, scale=0.5)
inference = ed.MetropolisHastings({mu: qmu}, {mu: proposal_mu},
data={x: np.zeros(10, dtype=np.float32)})
init
__init__(
latent_vars,
proposal_vars,
data=None
)
Create an inference algorithm.
proposal_vars
: dict of RandomVariable to RandomVariable. Collection of random variables to perform inference on; each is binded to a proposal distribution \(g(z' \mid z)\).build_update
build_update()
Draw sample from proposal conditional on last sample. Then accept or reject the sample based on the ratio,
\(\text{ratio} = \log p(x, z^{\text{new}}) - \log p(x, z^{\text{old}}) - \log g(z^{\text{new}} \mid z^{\text{old}}) + \log g(z^{\text{old}} \mid z^{\text{new}})\)
The updates assume each Empirical random variable is directly parameterized by tf.Variable
s.
finalize
finalize()
Function to call after convergence.
initialize
initialize(
*args,
**kwargs
)
print_progress
print_progress(info_dict)
Print progress to output.
run
run(
variables=None,
use_coordinator=True,
*args,
**kwargs
)
A simple wrapper to run inference.
initialize
.update
for self.n_iter
iterations.print_progress
.finalize
.To customize the way inference is run, run these steps individually.
variables
: list. A list of TensorFlow variables to initialize during inference. Default is to initialize all variables (this includes reinitializing variables that were already initialized). To avoid initializing any variables, pass in an empty list.use_coordinator
: bool. Whether to start and stop queue runners during inference using a TensorFlow coordinator. For example, queue runners are necessary for batch training with file readers. *args, **kwargs: Passed into initialize
.update
update(feed_dict=None)
Run one iteration of sampling.
feed_dict
: dict. Feed dictionary for a TensorFlow session run. It is used to feed placeholders that are not fed during initialization.dict. Dictionary of algorithm-specific information. In this case, the acceptance rate of samples since (and including) this iteration.
We run the increment of t
separately from other ops. Whether the others op run with the t
before incrementing or after incrementing depends on which is run faster in the TensorFlow graph. Running it separately forces a consistent behavior.
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092.