Before starting the description of the probability distributions, we want to impose on the reader the essential feature that a model is an interpretation of a real phenomenon that fits its characteristics to some degree of approximation rather than an explanation that would require the model to be “true”. In short, there is no such thing as a “true model”, even though some models are more appropriate than others!Jean-Michel Marin and Christian P. Robert in Bayesian Core: a practical approach to computational Bayesian statistics.
0 responses to “On “true” models”
In the early days of the Text Encoding Initiative (TEI), Micheal Sperberg-McQueen and Lou Burnard said something quite similar:
"It is important to remember that every document type definition is an interpretation of a text. There is no single DTD which encompasses any kind of absolute truth about a text, although it may be convenient to privilege some DTDs above others for particular types of analysis." TEI P2, Chapter 2. (1993).
I have often thought about that while listening to arguments over the "correct" basis for ontological models.
During all the moving around I missed your comment. I find interesting all the meaning that we attach (or read in) to models, in particular the ones that are often quoted by the media. By the way, you have an interesting blog.