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Uninformed priors An uninformed prior does not provide any additional information to a <strong>Bayes</strong>ian inference other than establishing a possible range. For example, in some circumstances we may not have any information about the likely prevalence of infection within a herd. We might assume that, for a particular disease, the prevalence is likely to range from 0% to 30% and that any value within this range is equally as likely as any other value. This constitutes a uniform prior, Uniform(0,0.3), and has no influence on the <strong>Bayes</strong>ian inference calculation, apart from establishing a range
Other examples of uninformed priors We might want to estimate the average number of disease outbreaks per year (λ) If we assume that each outbreak is independent of every other outbreak and that there is a constant and continuous probability of a disease outbreak occurring throughout the year, then the outbreaks follow a Poisson process.
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 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 36 and 37: If we have positives?
Page 38 and 39: Second order bayesian inference Tes

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