Bayes - Medreonet

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What form does <strong>Bayes</strong>’ theorem assume when dealing with distributions Note: Σ j pr(H j )pr(D|H j ) = Sum of all probabilities of the data given all the hypotheses. It is very difficult to know all probabilities of data given all the hypotheses. Nevertheless, since this expression is the same denominator for each pr(H i ǀD): pr(H i ǀD)∝pr(H i )pr(DǀH i ) and Σ j pr(H j )pr(DǀH j ) becomes a normalization constant to make the sum of all numerators = 1
How to apply bayesian inference Let’s imagine that we test a flock of 50 sheep with a test with 93% sensitivity and 95% specificity We get no positive results What is the probability distribution of the number of What is the probability distribution of the number of (undetected) infected animals in the flock?
Page 2 and 3: Background
Page 4 and 5: Inductive logical schemes cause cau
Page 6 and 7: Two rules are important to apply Ba
Page 8 and 9: P ( B A) = P ( B) ⋅ P( A B) P( A)
Page 10 and 11: P(hypothesis) = probability of the
Page 12 and 13: Bayesian inference Bayesian infere
Page 14 and 15: Bayesian inference Bayesian infere
Page 16 and 17: Uninformed priors An uninformed pr
Page 18 and 19: Other examples of uninformed priors
Page 20 and 21: Other examples of uninformed priors
Page 22 and 23: Informed priors An informed prior
Page 24 and 25: Step 2. Likelihood functions The l
Page 26 and 27: Step 3. Posterior distributions Th
Page 30 and 31: How to apply bayesian inference Tes
Page 36 and 37: If we have positives?
Page 38 and 39: Second order bayesian inference Tes

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