[xitrium]

If you care about quantifying uncertainty, knowing about Bayesian methods is a good idea I don't see represented here yet. I care so much about uncertainty quantification and propagation that I work on the Stan project[0] which has an extremely complete manual (600+ pages) and many case studies illustrating different problems. Full Bayesian inference such as that provided by Stan's Hamiltonian Monte Carlo inference algorithm is fairly computationally expensive so if you have more data than fits into RAM on a large server, you might be better served by some approximate methods (but note the required assumptions) like INLA[1].

[0] http://mc-stan.org/ [1] http://www.r-inla.org/

[peatmoss]

I think this is an important point. Having worked in / proximate to public policy kinds of problems, Bayesian methods have some really great properties:

1. easier interpretation of results than frequentist methods for lay people (business strata, elected officials, or other decision makers)

2. Uncertainty can be quantified and visualized reasonably well, which helps decision makers not think of stats as a magic box that produces a single answer.

3. Sensitivity analysis can be placed right up front: selection of priors representative of the beliefs of differing opinions / ideologies can inform decision makers of when they should consider changing their minds, and when they might still hold out.

Downsides of Bayesian methods:

1) Conceptually more involved than typical maximum likelihood estimation methods

2) Computationally expensive

3) Methods might not be as well known to a nominally stats-savvy audience.

[smartera]

I have also used Bayesian quantification of uncertainty in pricing forecast models. Decision makers love a measure of uncertainty when one recommends a pricing scenario that can have significant impact on revenue. Also, you get the chance to build multilayer models to combine knowledge from independent samples. PyMC3 is fantastic for building these models within Jupyter and Gelman's Bayesian Data Analysis is a great introduction for different Bayesian model applications.

[hobolord]

do you have a recommended guide/textbook on learning stan? I've recently started doing more bayesian analysis, mainly bayesian estimation supercedes the t-test.

[wishart_washy]

As someone who uses Stan - I would recommend reading the Stan reference documentation, it's essentially a textbook.

Also, get used to reading the Stan forums on Discourse. Happy Stanning