Even for the most currmudgeonly frequentist it is sometimes refreshing to move your effort from deriving frequentist estimators for intractable models, to using the damn Bayesian ones, which fail in different and interesting ways than you are used to. If it works and you are feeling fancy you might then justify your Bayesian method on frequentist grounds, which washes away the sin.

Here are some scattered tidbits about getting into it. No attempt is made to be comprehensive, novel, or to even expert.

## Prior choice

Is weird and important. Here are some argumentative and disputed rules of thumb.

## Learning

## Workflow

Everyone references Bayesian Data Analysis (free online, with copious learning notes) as a first stopping point. It is simple and readable.

The visualisation howto from, basically, the Stan team, is a deeper than it sounds. (Gabry et al. 2019)

Michael Betancourtβs examples, for example his workflow tips, are a good start.

Chris Fonnesbeckβs workshop in R.

Intro to Stan for econometrics.

See also BAT the Bayesian Analysis Toolkit, which does sophisticated Bayes modelling although AFAICT uses a fairly basic Sampler?

Notes on Rao-Blackwellisation for doing faster MCMC inference, and even handling discrete parameters in Stan.

## Nonparametrics

Dirichlet processes, Gaussian Process regression etc. π

## Tools

## Applied

## References

*arXiv:2110.11216 [Cs, Math, Stat]*, October.

*Synthese*85 (3): 475β506.

*arXiv:1705.02780 [Cond-Mat]*, May.

*Bayesian Theory*. 1 edition. Chichester: Wiley.

*arXiv Preprint arXiv:1509.07164*.

*Learning to Learn*, 95β133. Springer, Boston, MA.

*arXiv:2012.00152 [Cs, Stat]*, November.

*Journal of the Royal Statistical Society: Series A (Statistics in Society)*182 (2): 389β402.

*Bayesian Analysis*1 (3): 515β34.

*Bayesian Data Analysis*. 3 edition. Chapman & Hall/CRC texts in statistical science. Boca Raton: Chapman and Hall/CRC.

*Sociological Methodology*25: 165β73.

*arXiv:1206.3255*, June.

*Journal of Statistical Software*76 (1).

*Proceedings of the IEEE*58 (5): 632β43.

*How to Measure Anything: Finding the Value of Intangibles in Business*. 3 edition. Hoboken, New Jersey: Wiley.

*arXiv:1611.01241 [Stat]*, November.

*Frontiers in Applied Mathematics and Statistics*3.

*Bayesian Models of Perception and Action*.

*Network: Computation in Neural Systems*6 (3): 469β505.

*Neural Computation*11 (5): 1035β68.

*JMLR*, April.

*arXiv:2004.06425 [Stat]*, December.

*Statistical Rethinking: A Bayesian Course with Examples in R and STAN*. Boca Raton: CRC Press.

*Sociological Methodology*25: 111β63.

*The Bayesian choice: from decision-theoretic foundations to computational implementation*. 2nd ed. Springer texts in statistics. New York: Springer.

*Theory of Statistics*. Springer Series in Statistics. New York, NY: Springer Science & Business Media.

*Nature Reviews Methods Primers*1 (1): 1β26.

*Acta Numerica*19: 451β559.

*The American Statistician*42 (4): 278β80.

*Journal of Econometrics*, Information and Entropy Econometrics, 107 (1): 41β50.

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