Principled bayesian workflow to PyMC3

rosgori · January 26, 2019, 2:25am

Has anyone translated this jupyter notebook to PyMC3?

betanalpha/jupyter_case_studies/blob/master/principled_bayesian_workflow/principled_bayesian_workflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Towards A Principled Bayesian Workflow\n",
    "_*Michael Betancourt*_<br>\n",
    "_*October 2018*_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a probabilistic model and a particular observation, Bayesian inference is straightforward to implement.  Inferences, and any decisions based upon them, follow immediately in the form of expectations with respect to the corresponding posterior distribution.  Building a probabilistic model that is useful in a given application, however, is a far more open-ended challenge.  Unfortunately the process of model building is often ignored in introductory texts, leaving practitioners to piece together their own model building workflow from potentially incomplete or even inconsistent heuristics.\n",
    "\n",
    "In this case study I introduce a _principled_ workflow for building\n",
    "and evaluating probabilistic models in Bayesian inference.  This workflow is\n",
    "based on recent research in collaboration with Dan Simpson, Aki Vehtari, Sean\n",

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junpenglao · January 26, 2019, 7:27pm

I think @_eigenfoo did some thing very close to that purpose: https://eigenfoo.xyz/bayesian-modelling-cookbook/

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Principled bayesian workflow to PyMC3

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