| 21. | A particularly important application of Dirichlet processes is as a prior probability distribution in infinite mixture models.
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| 22. | Uniform distribution probability density was proposed by Thomas Bayes to represent ignorance of prior probabilities in Bayesian inference.
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| 23. | Classical probability can offer prior probabilities that reflect ignorance which often seems appropriate before an experiment is conducted.
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| 24. | It uses the obtained value of F to estimate the prior probability of the null hypothesis being true.
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| 25. | Fiducial inference can be interpreted as an attempt to perform inverse probability without calling on prior probability distributions.
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| 26. | Probabilities before an inference are known as prior probabilities, and probabilities after are known as posterior probabilities.
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| 27. | Bayesian statistics also bring in " prior probabilities " where you can bias sources based on past performance.
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| 28. | The objective and subjective variants of Bayesian probability differ mainly in their interpretation and construction of the prior probability.
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| 29. | This requires that either the signal statistics is known or a prior probability for the signal can be specified.
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| 30. | In Bayesian probability, one needs to establish prior probabilities for the various hypotheses before applying Bayes'theorem.
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