Rob Hicks (Posts about mcmc)http://rlhick.people.wm.edu/enTue, 13 Feb 2024 12:35:36 GMTNikola (getnikola.com)http://blogs.law.harvard.edu/tech/rssTensorflow with Custom Likelihood Functionshttp://rlhick.people.wm.edu/posts/custom-likes-tensorflow.html<div><p>
This post builds on earlier ones dealing with <a href="http://rlhick.people.wm.edu/posts/estimating-custom-mle.html">custom likelihood
functions in python</a> and maximum likelihood estimation with <a href="http://rlhick.people.wm.edu/posts/mle-autograd.html">auto
differentiation</a>. This post is approaching tensorflow from an
econometrics perspective and is based on a series of tests and notes I
developed for using tensorflow for some of my work. In early
explorations of tensorflow nearly all of the examples I encountered
were from a machine learning perspective, making it difficult to fit
code examples to econometric problems. For the tensorflow uninitiated
who want to dive in (like me!), I hope this will prove useful.
</p>
<p>
The goals of the post:
</p>
<ol class="org-ol">
<li>Some tensorflow basics I wish I had known before I started this work</li>
<li>Define a custom log-likelihood function in tensorflow and perform
differentiation over model parameters to illustrate how, under the
hood, tensorflow's model graph is designed to calculate derivatives
"free of charge" (no programming required and very little to no
additional compute time).</li>
<li>Use the tensorflow log-likelihood to estimate a maximum likelihood
model using <code>tensorflow_probability.optimizer</code> capabilities.</li>
<li>Illustrate how the <code>tensorflow.probability.mcmc</code> libraries can be
used with custom log-likelihoods.</li>
</ol>
<p><a href="http://rlhick.people.wm.edu/posts/custom-likes-tensorflow.html">Read moreā¦</a> (12 min remaining to read)</p></div>likelihoodmaximum likelihoodmcmctensorflowhttp://rlhick.people.wm.edu/posts/custom-likes-tensorflow.htmlMon, 24 Feb 2020 08:15:50 GMT