Bayes - Medreonet

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How to apply bayesian inference Tested= 50 Positive = 0 Sensitivity of the test= 93% Specificity of the test= 95% Normalized=Posterior/Sum Infected animals in the sample Prior Binomial likelihood Posterior Nurmalized posterior 0 0,019608 1 0,019608 0,93 1 0,019608 0,07 0,001373 0,0651 2 0,019608 0,0049 9,61E-05 0,004557 3 0,019608 0,000343 6,73E-06 0,00031899 49 0,019608 2,56924E-57 5,04E-59 2,38939E-57 50 0,019608 1,79847E-58 3,53E-60 1,67257E-58
The result is: <strong>Bayes</strong>ian inference 1 0,9 0,8 0,7 Probability 0,6 0,5 0,4 0,3 0,2 0,1 0 0 1 2 3 4 5 Infected animals in the sample
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 28 and 29: What form does Bayes’ theorem ass
Page 30 and 31: How to apply bayesian inference Tes
Page 32 and 33: 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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