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Jul 26, 2020	»	Frequentist Approach vs. Bayesian Approach 3 min; updated Feb 12, 2023 The Frequentist Approach Observe data. Assume the data were generated randomly, e.g. by nature, by designing a survey, etc. Make assumptions on the generating process, e.g. i.i.d., Gaussian, etc. Associate the generating process to some object of interest, e.g. a parameter, a density, etc. Assuming that the object is unknown but fixed, try to find it, e.g. estimate it, test a hypothesis about it, etc. The Bayesian Approach Observe data. Assume the data are generated randomly by some process. Under some assumptions, e.g. parametric distribution, associate the process with some fixed object. We have a prior belief about the object. Using the data, we want to update that belief into a posterior belief. See Galef’s “Rethinking Identity” for a fun exploration of Bayesian vs. Frequentists rivalry . ...
Oct 17, 2017	»	Other Measures of Similarity in NN 4 min; updated Feb 12, 2023 This list is not exhaustive. For example, lists multiple distance and similarity measures for different kinds of data: numerical (12), boolean (8), string (5), images & color (2), geospatial & temporal (4), and general & mixed (1). Nominal variables are variables that have two or more categories, but which do not have an intrinsic order. Dichotomous variables are nominal variables which have only two categories. Dichotomous attributes (e.g. yes-or-no) are distinct from binary attributes (present vs. absent), e.g. binary attributes may be asymmetric in that co-presence suggests similarity, but co-absence may or may not be considered evidence of similarity. . ...

The Frequentist Approach

The Bayesian Approach