Why Bayes?
Alexandre Bouchard-Côté
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