Data analysis recipes: Probability calculus for inference
arXiv:1205.4446
Abstract
In this pedagogical text aimed at those wanting to start thinking about or brush up on probabilistic inference, I review the rules by which probability distribution functions can (and cannot) be combined. I connect these rules to the operations performed in probabilistic data analysis. Dimensional analysis is emphasized as a valuable tool for helping to construct non-wrong probabilistic statements. The applications of probability calculus in constructing likelihoods, marginalized likelihoods, posterior probabilities, and posterior predictions are all discussed.
a chapter from a non-existent book
References in corpus (2)
Cited by in corpus (6)
- Data analysis recipes: Using Markov Chain Monte Carlo
- How not to obtain the redshift distribution from probabilistic redshift estimates: Under what conditions is it not inappropriate to estimate the redshift distribution N(z) by stacking photo-z PDFs?
- The 31-year Rotation History of the Millisecond Pulsar J1939+2134 (B1937+21)
- Data Analysis Recipes: Products of multivariate Gaussians in Bayesian inferences
- A likelihood function for the Gaia Data
- Rotation Curves in z~1-2 Star-Forming Disks: Comparison of Dark Matter Fractions and Disk Properties for Different Fitting Methods