Why Bayes?

Flexibility and generality

• Address most data analysis issues (missing data, non-standard data types, non-iid, weird loss functions, adding expert knowledge, …)
  • Bayesian analysis: address those in a (semi) automated fashion / principled framework (“reductionist”)
    • Thanks to the Bayes estimator 3-steps process!
    • Reductionism can be bad or good (main con of reductionism is computational)
  • Frequentist statistics: every problem is a new problem

Implementation complexity

• Efficient in analyst’s time (thanks to PPLs)
• Harder to scale computationally
• \(\Longrightarrow\) shines on small data problems (there a much more of those than the “big data” hype would like you to think)

Statistical properties

• Optimal if the model is well-specified
• Sub-optimal in certain cases when the model is mis-specified
  • Thankfully the modelling flexibility makes it easier to build better models
  • Important to make model checks