Abstract
In the previous chapter, we developed a general probabilistic model of the measurement process. We have now to learn how to use it in practice. This implies being able to apply correctly the principles and methods of statistical-probabilistic inference. Basically a probabilistic inference is a way to learn from data. In the current practice of measurement, statistical methods are applied routinely, sometimes without a full understanding of their implications. This is not good practice.
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Notes
- 1.
Here, the term “random variable” would be appropriate, since the Bernoullian model is intended to describe randomness.
- 2.
In my view, there is no substantial difference between models and theories: theories, in my view, are just very general models.
- 3.
The interpretation of this rule as a way for assessing the probability of causes, after observing the effects, is traceable to Laplace himself [13].
- 4.
We will consider a similar example in Sect. 9.1.3 to probe further this important topic.
- 5.
They will be addressed in Chap. 11 and constitute a very important application of the ideas here presented.
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Rossi, G.B. (2014). Inference in Measurement. In: Measurement and Probability. Springer Series in Measurement Science and Technology. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8825-0_6
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