Wikipedia: Revising opinions in statistics

Difference (from prior minor revision) (no other diffs)

Changed: 1,4c1,4

Researchers who use Personal Probability can proceed as follows:
#A Statistical Model for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.
#The researcher describes his opinion about the unknown mean as having a Normal Distribution centered at 10 with a Standard Deviation of 2. This would be called the researcher's prior distribution for the mean.
#With the [Likelihood Function]? of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [Bayes Theorem]?) the appropriate opinion consistent with both sources of information. This is called the posterior distribution.

Researchers who use personal probability can proceed as follows:
#A statistical model for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.
#The researcher describes his opinion about the unknown mean as having a normal distribution centered at 10 with a standard deviation of 2. This would be called the researcher's prior distribution for the mean.
#With the [likelihood function]? of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [Bayes' Theorem]?) the appropriate opinion consistent with both sources of information. This is called the posterior distribution.

Changed: 6c6

back to Statistical Theory -- Applied Statistics

back to statistical theory -- applied statistics

Researchers who use personal probability can proceed as follows:

A statistical model for the data generating process is assumed. The model might specify that the data follows a normal distribution with an unknown mean.
The researcher describes his opinion about the unknown mean as having a normal distribution centered at 10 with a standard deviation of 2. This would be called the researcher's prior distribution for the mean.
With the [likelihood function]? of the observed data and the probabilistic description of his opinion, the researcher can calculate (using [Bayes' Theorem]?) the appropriate opinion consistent with both sources of information. This is called the posterior distribution.

back to statistical theory -- applied statistics