Krushke - Doing Bayesian Data Analysis A Tutorial with R, JAGS, and Stan:

"The first idea is that Bayesian inference is reallocation of credibility across possibilities. The second foundational idea is that the possibilities, over which we allocate credibility, are parameter values in meaningful mathematical models."

"Another example of Bayesian inference has been immortalized in the words of the fictional detective Sherlock Holmes, who often said to his sidekick, Doctor Watson: “How often have I said to you that when you have eliminated the impossible, whatever remains however improbable, must be the truth?” (Doyle, 1890, chap. 6). Although this reasoning was not described by Holmes or Watson or Doyle as Bayesian inference, it is."

"All scientific data have some degree of “noise” in their values. The techniques of data analysis are designed to infer underlying trends from noisy data. Unlike Sherlock Holmes, who could make an observation and completely rule out some possible causes, we can collect data and only incrementally adjust the credibility of some possible trends. We will see many realistic examples later in the book. The beauty of Bayesian analysis is that the mathematics reveal exactly how much to re-allocate credibility in realistic probabilistic situations."

"The essence of Bayesian inference is reallocation of credibility across possibilities. The distribution of credibility initially reflects prior knowledge about the possibilities, which can be quite vague. Then new data are observed, and the credibility is re-allocated. Possibilities that are consistent with the data garner more credibility, while possibilities that are not consistent with the data lose credibility. Bayesian analysis is the mathematics of re-allocating credibility in a logically coherent and precise way."

More to come. Stay tuned.

Useful links:
https://statswithr.github.io/book/bayesian-model-choice.html
http://www.ling.uni-potsdam.de/~vasishth/pdfs/StatMethLingPart2.pdf
https://blog.efpsa.org/2014/11/17/bayesian-statistics-what-is-it-and-why-do-we-need-it-2/
https://link.springer.com/article/10.3758/s13423-015-0947-8