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<h1 id="ed.metropolishastings">ed.MetropolisHastings</h1>
<h2 id="class-metropolishastings">Class <code>MetropolisHastings</code></h2>
<p>Inherits From: <a href="../ed/MonteCarlo"><code>MonteCarlo</code></a></p>
<h3 id="aliases">Aliases:</h3>
<ul>
<li>Class <code>ed.MetropolisHastings</code></li>
<li>Class <code>ed.inferences.MetropolisHastings</code></li>
</ul>
<p>Defined in <a href="https://github.com/blei-lab/edward/tree/master/edward/inferences/metropolis_hastings.py"><code>edward/inferences/metropolis_hastings.py</code></a>.</p>
<p>Metropolis-Hastings <span class="citation" data-cites="metropolis1953equation hastings1970monte">(Hastings, 1970; Metropolis, Rosenbluth, Rosenbluth, Teller, & Teller, 1953)</span>.</p>
<h4 id="notes">Notes</h4>
<p>In conditional inference, we infer <span class="math inline">\(z\)</span> in <span class="math inline">\(p(z, \beta \mid x)\)</span> while fixing inference over <span class="math inline">\(\beta\)</span> using another distribution <span class="math inline">\(q(\beta)\)</span>. To calculate the acceptance ratio, <code>MetropolisHastings</code> uses an estimate of the marginal density,</p>
<p><span class="math inline">\(p(x, z) = \mathbb{E}_{q(\beta)} [ p(x, z, \beta) ] \approx p(x, z, \beta^*)\)</span></p>
<p>leveraging a single Monte Carlo sample, where <span class="math inline">\(\beta^* \sim q(\beta)\)</span>. This is unbiased (and therefore asymptotically exact as a pseudo-marginal method) if <span class="math inline">\(q(\beta) = p(\beta \mid x)\)</span>.</p>
<p><code>MetropolisHastings</code> assumes the proposal distribution has the same support as the prior. The <code>auto_transform</code> attribute in the method <code>initialize()</code> is not applicable.</p>
<h4 id="examples">Examples</h4>
<pre class="python"><code>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)})</code></pre>
<h2 id="methods">Methods</h2>
<h3 id="__init__">
<code><strong>init</strong></code>
</h3>
<pre class="python"><code>__init__(
latent_vars,
proposal_vars,
data=None
)</code></pre>
<p>Create an inference algorithm.</p>
<h4 id="args">Args:</h4>
<ul>
<li><b><code>proposal_vars</code></b>: dict of RandomVariable to RandomVariable. Collection of random variables to perform inference on; each is binded to a proposal distribution <span class="math inline">\(g(z' \mid z)\)</span>.</li>
</ul>
<h3 id="build_update">
<code>build_update</code>
</h3>
<pre class="python"><code>build_update()</code></pre>
<p>Draw sample from proposal conditional on last sample. Then accept or reject the sample based on the ratio,</p>
<p><span class="math inline">\(\text{ratio} = \log p(x, z^{\text{new}}) - \log p(x, z^{\text{old}}) - \log g(z^{\text{new}} \mid z^{\text{old}}) + \log g(z^{\text{old}} \mid z^{\text{new}})\)</span></p>
<h4 id="notes-1">Notes</h4>
<p>The updates assume each Empirical random variable is directly parameterized by <code>tf.Variable</code>s.</p>
<h3 id="finalize">
<code>finalize</code>
</h3>
<pre class="python"><code>finalize()</code></pre>
<p>Function to call after convergence.</p>
<h3 id="initialize">
<code>initialize</code>
</h3>
<pre class="python"><code>initialize(
*args,
**kwargs
)</code></pre>
<h3 id="print_progress">
<code>print_progress</code>
</h3>
<pre class="python"><code>print_progress(info_dict)</code></pre>
<p>Print progress to output.</p>
<h3 id="run">
<code>run</code>
</h3>
<pre class="python"><code>run(
variables=None,
use_coordinator=True,
*args,
**kwargs
)</code></pre>
<p>A simple wrapper to run inference.</p>
<ol type="1">
<li>Initialize algorithm via <code>initialize</code>.</li>
<li>(Optional) Build a TensorFlow summary writer for TensorBoard.</li>
<li>(Optional) Initialize TensorFlow variables.</li>
<li>(Optional) Start queue runners.</li>
<li>Run <code>update</code> for <code>self.n_iter</code> iterations.</li>
<li>While running, <code>print_progress</code>.</li>
<li>Finalize algorithm via <code>finalize</code>.</li>
<li>(Optional) Stop queue runners.</li>
</ol>
<p>To customize the way inference is run, run these steps individually.</p>
<h4 id="args-1">Args:</h4>
<ul>
<li><b><code>variables</code></b>: 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.</li>
<li><b><code>use_coordinator</code></b>: 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 <code>initialize</code>.</li>
</ul>
<h3 id="update">
<code>update</code>
</h3>
<pre class="python"><code>update(feed_dict=None)</code></pre>
<p>Run one iteration of sampling.</p>
<h4 id="args-2">Args:</h4>
<ul>
<li><b><code>feed_dict</code></b>: dict. Feed dictionary for a TensorFlow session run. It is used to feed placeholders that are not fed during initialization.</li>
</ul>
<h4 id="returns">Returns:</h4>
<p>dict. Dictionary of algorithm-specific information. In this case, the acceptance rate of samples since (and including) this iteration.</p>
<h4 id="notes-2">Notes</h4>
<p>We run the increment of <code>t</code> separately from other ops. Whether the others op run with the <code>t</code> before incrementing or after incrementing depends on which is run faster in the TensorFlow graph. Running it separately forces a consistent behavior.</p>
<div id="refs" class="references">
<div id="ref-hastings1970monte">
<p>Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. <em>Biometrika</em>, <em>57</em>(1), 97–109.</p>
</div>
<div id="ref-metropolis1953equation">
<p>Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. <em>The Journal of Chemical Physics</em>, <em>21</em>(6), 1087–1092.</p>
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