Template repository for nbsphinx

This repository serves as a template for using nbsphinx to compile a collection of jupyter notebooks into html and, via LaTeX, pdf.

• Plotting setup
  • Show available fonts
  • Add latin modern fonts
  • Set matplotlib to use Latin Modern fonts
  • Check rcParams after update and list fonts available to matplotlib
  • Create a test plot
• Introductory review of applied probabilistic inference
  • Setup
    • Install libraries
    • Add latin modern fonts
    • Set matplotlib to use latin modern fonts
  • Modeling process
    • Observing the world through the lens of probability
      • Systems, environments, and observations
      • The space of observational models and the true data generating process
    • The practical reality of model construction
    • The process of inference
    • Workflow overview
    • Example generative models
      • Univariate normal model
      • Multivariate normal models
  • “Build, compute, critique, repeat”: Box’s loop in iteration through Betancourt’s principled Bayesian workflow
    • Setup
      • Load libraries
      • define colors
    • Poisson process for arbitrary detector count data
      • Sample data
      • Generative model specification
      • Plot prior predictive distribution
      • Fit to simulated data
      • Fit observations and evaluate
    • Account for measurement device failure
      • Update the generative model specification
      • Simulate the updated model
      • Fit Simulated Observations and Evaluate
      • Fit observations and evaluate
    • Account for the distinction between functioning and malfunctioning measurement devices
      • Update the generative model
      • Plot prior predictive distribution
      • Fit to simulated data
      • Fit observations and evaluate
    • Account for upper limit of detection
• References