Predictive updating methods with application to bayesian classification
Some "softening" approaches utilize the concepts and techniques developed in the fuzzy set theory, the theory of possibility, and Dempster-Shafer theory.
The following Figure illustrates the three major schools of thought; namely, the Classical (attributed to Laplace), Relative Frequency (attributed to Fisher), and Bayesian (attributed to Savage). Plato, Jan von, Creating Modern Probability, Cambridge University Press, 1994.
I'll also overview several other techniques in less depth.
After the crisis is over, things may look different and historians of statistics may cast the event as one in a series of steps in "building upon a foundation".One of the main reasons for using cross-validation instead of using the conventional validation (e.g.partitioning the data set into two sets of 70% for training and 30% for test) is that there is not enough data available to partition it into separate training and test sets without losing significant modelling or testing capability.In these cases, a fair way to properly estimate model prediction performance is to use cross-validation as a powerful general technique.
Suppose we have a model with one or more unknown parameters, and a data set to which the model can be fit (the training data set).
This usually involves using database techniques such as spatial indices.