bayes priorの例文

例文

1. It will be shown in the next section that Jeffreys prior probability results in posterior probabilities ( when multiplied by the binomial likelihood function ) that are intermediate between the posterior probability results of the Haldane and Bayes prior probabilities.
2. For symmetric distributions, the Bayes prior Beta ( 1, 1 ) results in the most " peaky " and highest posterior distributions and the Haldane prior Beta ( 0, 0 ) results in the flattest and lowest peak distribution.
3. In practice, the conditions 0 < s < n necessary for a mode to exist between both ends for the Bayes prior are usually met, and therefore the Bayes prior ( as long as 0 < s < n ) results in a posterior mode located between both ends of the domain.
4. In practice, the conditions 0 < s < n necessary for a mode to exist between both ends for the Bayes prior are usually met, and therefore the Bayes prior ( as long as 0 < s < n ) results in a posterior mode located between both ends of the domain.