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Posted on January 4, 2020 at 3:00am 0 Comments 0 Likes
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…
ContinuePosted on January 3, 2020 at 4:30am 0 Comments 0 Likes
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…
ContinuePosted on December 19, 2019 at 9:00am 1 Comment 2 Likes
This post is the third one of a series regarding loops in R an Python.
The first one was Different kinds of loops in R. The recommendation…
ContinuePosted on November 13, 2019 at 2:00am 0 Comments 2 Likes
The importance of completeness of linear regressions is an often-discussed issue. By leaving out relevant variables the coefficients might be inconsistent.
But why on earth?!
Assuming a linear complete model of the form:
z = a + bx + cy + ε.
Where z is supposed to be dependent, x and y are independent and ε is the error term.
Now we drop y to check…
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