Bayes - Medreonet

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Step 2. Likelihood functions The likelihood function calculates the probability of observing the data for a given value of the prior estimate of a parameter The shape of the likelihood function embodies the amount of information contained in the data If the information is limited, the likelihood function will be broadly distributed, whereas if the information is significant, the likelihood function will be tightly focused around a particular parameter value
Likelihood functions There are a number of useful probability distribution functions that can be used as likelihood functions, depending on the circumstances These include the Binomial, Poisson, hypergeometric and These include the Binomial, Poisson, hypergeometric and negative binomial
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 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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