Charles Lindsey
Revionics, an Aptos Company

Abstract

TensorFlow Probability is a powerful library for statistical analysis in Python. Using TensorFlow Probability’s implementation of Bayesian methods, modelers can incorporate prior information and obtain parameter estimates and a quantified degree of belief in the results. Resampling methods like Markov Chain Monte Carlo can also be used to perform Bayesian analysis. As an alternative, we show how to use numerical optimization to estimate model parameters, and then show how numerical differentiation can be used to get a quantified degree of belief. How to perform simulation in Python to corroborate our results is also demonstrated.

Keywords

Bayesian statistics, resampling, maximum likelihood, numerical differentiation

DOI

10.25080/gerudo-f2bc6f59-005

Bibtex entry

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Copyright © 2023 Charles Lindsey. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.