Bayes - Medreonet

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Bayesian inference Bayesian inference is a useful, powerful technique whereby newly acquired empirical data can be combined with existing information, whether that information is itself based on pre-existing empirical data or on expert opinion, to improve an estimate of the parameter(s) used to characterise a distribution. Note: Bayesian inference is based on subjective probability (q.v.)
Bayesian inference Bayesian inference is a natural extension of Bayes’ theorem It provides a powerful and flexible means of learning from experience As new information becomes available it enables our existing knowledge to be easily and logically updated It explicitly acknowledges subjectivity and describes the learning process mathematically We begin with an opinion, however vague, and modify it as new information becomes available
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 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 36 and 37: If we have positives?
Page 38 and 39: Second order bayesian inference Tes

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