data mining & machine learning dipl.-inf. christoph carl kling filedata mining & machine...
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Web Science & Technologies
University of Koblenz ▪ Landau, Germany
Data Mining & Machine Learning
Dipl.-Inf. Christoph Carl Kling
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Probability Theoryn = 1 n >= 1
Bernoulli = Binomial for n = 1 Binomial
k = 2
k > 2
Multinomial
100
1
Multinomial for n = 1
p
n → ∞
Gaussian
MulivariateGaussian
1 2 3 k
p
number of successes
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Experiment
Observations c (our Data)Hidden (latent) parameter p
Example: tossing a coin: 2 x head, 0 x tail
tail head
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Probabilistic models
p more likely is close to 0.5!
Prior probability
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Parameter Estimation
Maximum a posteriori estimation (MAP)
Bayesian inference
C. C. Kling NetHDP16 of 17
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Parameter Estimation
Maximum a posteriori estimation (MAP)
Bayesian inference