Читать книгу Informatics and Machine Learning. From Martingales to Metaheuristics онлайн

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Whenever you can list all the outcomes for some situation (like rolls on a six‐sided die), it is natural to think of the “probabilities” of those outcomes, where it is also natural for the outcome probabilities sum to one. So, with probability we assume there are “rules” (the probability assignments), and using those rules we make predictions on future outcomes. The rules are a mathematical framework, thus probability is a mathematical encapsulation of outcomes.

How did we get the “rules,” the probability assignments on outcomes? This is the realm of statistics, where we have a bunch of data and we want to distill any rules that we can, such as a complete set of outcomes (observed) and their assigned (estimated) probabilities. If the analysis to go from raw data to a probability model was somehow done in one step, then it could be said that statistics is whatever takes you from raw data to a probability model, and hopefully do so without dependency on a probability model. In practice, however, the statistical determination of a probability model suitable for a collection of data is like the identification of a physical law in mathematical form given raw data – it is math and a lot more, including an iterative creative/inventive process where models are attempted and discarded, and built from existing models.