.

There are many ways to deal with time-data. Sometimes one can use it as time-series to take possible trends into account. Sometimes this is not possible because time can not be arranged in a sequence. For example, if there are just weekdays (1 to 7) in a dataset over several month. In this case one could use one-hot-encoding. However, considering minutes or seconds of a day one-hot-encoding might lead to high complexity. Another approach is to make time cyclical. This approach leads to a…

ContinueAdded by Frank Raulf on January 26, 2020 at 4:00am — No Comments

For decision making, human perception tends to arrange probabilities into above 50% and below - which is plausible. For most probabilistic models in contrast, this is not the case at all. Frequently, resulting probabilities are neither normal distributed between zero and one with a mean of 0.5 nor correct in terms of absolute values. This is not seldom an issue accompanied with the existence of a minority class - in the underlying dataset.

*For example, if the result of a…*

Added by Frank Raulf on January 4, 2020 at 3:00am — No Comments

Bayesian inference is the re-allocation of credibilities over possibilities [Krutschke 2015]. This means that a bayesian statistician has an “a priori” opinion regarding the probabilities of an event:

p(d) (1)

By observing new data x, the statistician will adjust his opinions to get the "a posteriori" probabilities.

p(d|x) (2)

The conditional probability of an event d given x is the share of the joint…

ContinueAdded by Frank Raulf on January 3, 2020 at 4:30am — No Comments

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Posted 3 May 2021

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