# ed.Gibbs

## Class Gibbs

Inherits From: MonteCarlo

### Aliases:

• Class ed.Gibbs
• Class ed.inferences.Gibbs

Defined in edward/inferences/gibbs.py.

Gibbs sampling (Geman & Geman, 1984).

Note Gibbs assumes the proposal distribution has the same support as the prior. The auto_transform attribute in the method initialize() is not applicable.

#### Examples

x_data = np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 1])

p = Beta(1.0, 1.0)
x = Bernoulli(probs=p, sample_shape=10)

qp = Empirical(tf.Variable(tf.zeros(500)))
inference = ed.Gibbs({p: qp}, data={x: x_data})

## Methods

### init

__init__(
latent_vars,
proposal_vars=None,
data=None
)

Create an inference algorithm.

#### Args:

• proposal_vars: dict of RandomVariable to RandomVariable. Collection of random variables to perform inference on; each is binded to its complete conditionals which Gibbs cycles draws on. If not specified, default is to use ed.complete_conditional.

### build_update

build_update()

#### Notes

The updates assume each Empirical random variable is directly parameterized by tf.Variables.

### finalize

finalize()

Function to call after convergence.

### initialize

initialize(
scan_order='random',
*args,
**kwargs
)

Initialize inference algorithm. It initializes hyperparameters and builds ops for the algorithm’s computation graph.

#### Args:

• scan_order: list or str. The scan order for each Gibbs update. If list, it is the deterministic order of latent variables. An element in the list can be a RandomVariable or itself a list of RandomVariables (this defines a blocked Gibbs sampler). If ‘random’, will use a random order at each update.
print_progress(info_dict)

Print progress to output.

### run

run(
variables=None,
use_coordinator=True,
*args,
**kwargs
)

A simple wrapper to run inference.

1. Initialize algorithm via initialize.
2. (Optional) Build a TensorFlow summary writer for TensorBoard.
3. (Optional) Initialize TensorFlow variables.
4. (Optional) Start queue runners.
5. Run update for self.n_iter iterations.
6. While running, print_progress.
7. Finalize algorithm via finalize.
8. (Optional) Stop queue runners.

To customize the way inference is run, run these steps individually.

#### Args:

• 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.

#### Args:

• feed_dict: dict. Feed dictionary for a TensorFlow session run. It is used to feed placeholders that are not fed during initialization.

#### Returns:

dict. Dictionary of algorithm-specific information. In this case, the acceptance rate of samples since (and including) this iteration.

Geman, S., & Geman, D. (1984). Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, (6), 721–741.