Difference between revisions of "Bayesian inference"
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(The alternative is a classical frequentist approach.) |
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This is an iterative process which constantly updates the probability of the truth of the hypothesis as new data become available. | This is an iterative process which constantly updates the probability of the truth of the hypothesis as new data become available. | ||
| − | The alternative is a classical frequentist approach | + | The alternative is a classical frequentist approach,<ref>https://stats.stackexchange.com/questions/322464/what-is-the-difference-between-classical-frequentist-methods-and-likelihood-meth</ref> which takes far more time and money and may be inclusive. |
==References== | ==References== | ||
<references/> | <references/> | ||
| + | [[Category:Probability and Statistics]] | ||
[[Category:Statistics]] | [[Category:Statistics]] | ||
[[Category:Philosophy]] | [[Category:Philosophy]] | ||
Revision as of 01:35, May 29, 2020
Bayesian inference is an approach to statistics whereby all forms of uncertainty are described in terms of probability.
Bayesian inference applies Bayes' theorem to observations in order to infer the probability of the truth of an hypothesis.
This is an iterative process which constantly updates the probability of the truth of the hypothesis as new data become available.
The alternative is a classical frequentist approach,[1] which takes far more time and money and may be inclusive.